2012
Orio, N.; Rauber, A.; Rizo, D.
Introduction to the focused issue on music digital libraries Journal Article
In: International Journal on Digital Libraries, vol. 12, no. 2-3, pp. 51-52, 2012, ISSN: ISSN: 1432-5012.
@article{k294,
title = {Introduction to the focused issue on music digital libraries},
author = {N. Orio and A. Rauber and D. Rizo},
issn = {ISSN: 1432-5012},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {International Journal on Digital Libraries},
volume = {12},
number = {2-3},
pages = {51-52},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {article}
}
Abreu, J.
Detección de regularidades en contornos 2D, cálculo aproximado de medianas y su aplicación en tareas de clasificación PhD Thesis
2012.
Abstract | BibTeX | Tags: ISIC 2010, TIASA
@phdthesis{k293,
title = {Detección de regularidades en contornos 2D, cálculo aproximado de medianas y su aplicación en tareas de clasificación},
author = {J. Abreu},
editor = {J. R. Rico},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
organization = {Universidad de Alicante},
abstract = {In this work, we address two main problems: identifying the regularities and the construction of average contours from contours encoded by Freeman chain codes. Solutions for those problems are proposed which relies in the information gathered from the Levenshtein edit distance computation. We describe a new method for quantifying the regularity of contours and comparing them, when encoded by Freeman chain codes, in terms of a similarity criterion. The criterion used allows subsequences to be found from the minimal cost edit sequence that specifi and es an alignment of contour segments which are similar. Two external parameters adjust the similarity criterion. The information about each similar part is encoded by strings that represent an average contour region. An explanation of how to construct a prototype based on the identifi and ed regularities is also reviewed. The reliability of the prototypes is eva- luated by replacing contour groups, samples, by new prototypes used as the training set in a classifi and cation task. This way, the size of the data set can be reduced without sensibly affecting its representational power for classifi and cation purposes. Experimental results show that this scheme achieves a reduction in the size of the training data set of about 80% while the classifi and cation error only increases by 0.45% in one of the three data sets studied. Also this thesis presents a new fast algorithm for computing an approximation to the mean between two strings of characters representing a 2D shape and its application to a new Wilson-based editing proce dure. The approximate mean is built by including some symbols from the two original strings. Besides, a greedy approach to this algorithm is studied which allows to reduce the time required to computed an approximate mean. The new dataset editing scheme relaxes the criterion for deleting instances proposed by the Wilson editing procedure. In practice, not all instances misclassifi and ed by their near neighbors are pruned. Instead, an artifi and cial instance is added to the dataset in the hope of successfully classifying the instance in the future. The new artifi and cial instance is the approximated mean of the misclassifi and ed sample and its same-class nearest neighbor. Experiments carried over three widely known databases of contours show the proposed algorithms performs very well in computing the mean of two strings, outperforming methods proposed by other authors. Particularly the low computational time required by the heuristic approach make it very suitable when dealing with long length strings. Results also shows the propo- sed preprocessing scheme can reduce the classifi and cation error in about 83% of trials. There is empirical evidence that using the greedy approximation to compute the approximated mean does not affect the editing procedure performance. Finally, a new algorithm with which to compute an approximation to the mean of a set of strings is presented. The approximated mean is computed through the successive improvements of a partial solution. In each iteration, the edit distance from the partial solution to all the strings in the set are computed, thus accounting for the frequency of each of the edit operations in every position of the approximated mean. A goodness index for edit operations is later computed by multiplying their frequency by the cost. Each operation is tested, starting from that with the highest index, in order to verify whether applying it to the partial solution leads to an improvement. If successful, a new iteration begins from the new approximated mean. The algorithm fi and nishes after all the operations have been examined without a better solution being found. Comparative experiments involving Freeman chain codes encoding 2D shapes show that the quality of the approximated mean string is similar to other approaches but achieves a much faster convergence.},
keywords = {ISIC 2010, TIASA},
pubstate = {published},
tppubtype = {phdthesis}
}
In this work, we address two main problems: identifying the regularities and the construction of average contours from contours encoded by Freeman chain codes. Solutions for those problems are proposed which relies in the information gathered from the Levenshtein edit distance computation. We describe a new method for quantifying the regularity of contours and comparing them, when encoded by Freeman chain codes, in terms of a similarity criterion. The criterion used allows subsequences to be found from the minimal cost edit sequence that specifi and es an alignment of contour segments which are similar. Two external parameters adjust the similarity criterion. The information about each similar part is encoded by strings that represent an average contour region. An explanation of how to construct a prototype based on the identifi and ed regularities is also reviewed. The reliability of the prototypes is eva- luated by replacing contour groups, samples, by new prototypes used as the training set in a classifi and cation task. This way, the size of the data set can be reduced without sensibly affecting its representational power for classifi and cation purposes. Experimental results show that this scheme achieves a reduction in the size of the training data set of about 80% while the classifi and cation error only increases by 0.45% in one of the three data sets studied. Also this thesis presents a new fast algorithm for computing an approximation to the mean between two strings of characters representing a 2D shape and its application to a new Wilson-based editing proce dure. The approximate mean is built by including some symbols from the two original strings. Besides, a greedy approach to this algorithm is studied which allows to reduce the time required to computed an approximate mean. The new dataset editing scheme relaxes the criterion for deleting instances proposed by the Wilson editing procedure. In practice, not all instances misclassifi and ed by their near neighbors are pruned. Instead, an artifi and cial instance is added to the dataset in the hope of successfully classifying the instance in the future. The new artifi and cial instance is the approximated mean of the misclassifi and ed sample and its same-class nearest neighbor. Experiments carried over three widely known databases of contours show the proposed algorithms performs very well in computing the mean of two strings, outperforming methods proposed by other authors. Particularly the low computational time required by the heuristic approach make it very suitable when dealing with long length strings. Results also shows the propo- sed preprocessing scheme can reduce the classifi and cation error in about 83% of trials. There is empirical evidence that using the greedy approximation to compute the approximated mean does not affect the editing procedure performance. Finally, a new algorithm with which to compute an approximation to the mean of a set of strings is presented. The approximated mean is computed through the successive improvements of a partial solution. In each iteration, the edit distance from the partial solution to all the strings in the set are computed, thus accounting for the frequency of each of the edit operations in every position of the approximated mean. A goodness index for edit operations is later computed by multiplying their frequency by the cost. Each operation is tested, starting from that with the highest index, in order to verify whether applying it to the partial solution leads to an improvement. If successful, a new iteration begins from the new approximated mean. The algorithm fi and nishes after all the operations have been examined without a better solution being found. Comparative experiments involving Freeman chain codes encoding 2D shapes show that the quality of the approximated mean string is similar to other approaches but achieves a much faster convergence. Gallego-Sánchez, A. J.; Calera-Rubio, J.; López, D.
Structural Graph Extraction from Images Proceedings Article
In: Omatu, S.; Santana, Juan F. De Paz; González, S. Rodríguez; Molina, J. M.; a. M. Bernardos, (Ed.): Distributed Computing and Artificial Intelligence, pp. 717-724, Springer Berlin / Heidelberg, 2012, ISBN: 978-3-642-28764-0.
@inproceedings{k289,
title = {Structural Graph Extraction from Images},
author = {A. J. Gallego-Sánchez and J. Calera-Rubio and D. López},
editor = {S. Omatu and Juan F. De Paz Santana and S. Rodríguez González and J. M. Molina and a. M. Bernardos},
isbn = {978-3-642-28764-0},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {Distributed Computing and Artificial Intelligence},
pages = {717-724},
publisher = {Springer Berlin / Heidelberg},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Micó, L.; Oncina, J.
A log square average case algorithm to make insertions in fast similarity search Journal Article
In: Pattern Recognition Letters, vol. 33, no. 9, pp. 1060–1065, 2012.
Links | BibTeX | Tags: MIPRCV, TIASA
@article{k287,
title = {A log square average case algorithm to make insertions in fast similarity search},
author = {L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/287/prl.pdf},
year = {2012},
date = {2012-01-01},
journal = {Pattern Recognition Letters},
volume = {33},
number = {9},
pages = {1060–1065},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
Pertusa, A.; Iñesta, J. M.
Efficient methods for joint estimation of multiple fundamental frequencies in music signals Journal Article
In: EURASIP Journal on Advances in Signal Processing, vol. 2012, no. 1, pp. 27, 2012, ISSN: 1687-6180.
Abstract | BibTeX | Tags: DRIMS
@article{k286,
title = {Efficient methods for joint estimation of multiple fundamental frequencies in music signals},
author = {A. Pertusa and J. M. Iñesta},
issn = {1687-6180},
year = {2012},
date = {2012-01-01},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2012},
number = {1},
pages = {27},
abstract = {This study presents efficient techniques for multiple fundamental frequency estimation in music signals. The proposed methodology can infer harmonic patterns from a mixture considering interactions with other sources and evaluate them in a joint estimation scheme. For this purpose, a set of fundamental frequency candidates are first selected at each frame, and several hypothetical combinations of them are generated. Combinations are independently evaluated, and the most likely is selected taking into account the intensity and spectral smoothness of its inferred patterns. The method is extended considering adjacent frames in order to smooth the detection in time, and a pitch tracking stage is finally performed to increase the temporal coherence. The proposed algorithms were evaluated in MIREX contests yielding state of the art results with a very low computational burden.},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {article}
}
This study presents efficient techniques for multiple fundamental frequency estimation in music signals. The proposed methodology can infer harmonic patterns from a mixture considering interactions with other sources and evaluate them in a joint estimation scheme. For this purpose, a set of fundamental frequency candidates are first selected at each frame, and several hypothetical combinations of them are generated. Combinations are independently evaluated, and the most likely is selected taking into account the intensity and spectral smoothness of its inferred patterns. The method is extended considering adjacent frames in order to smooth the detection in time, and a pitch tracking stage is finally performed to increase the temporal coherence. The proposed algorithms were evaluated in MIREX contests yielding state of the art results with a very low computational burden. López-García, G.; Gallego, A. J.; Dalmau-Espert, J. L.; Molina-Carmona, R.; Compan-Rosique, P.
A Grammatical Approach to the Modeling of an Autonomous Robot Journal Article
In: International Journal of Interactive Multimedia and Artificial Intelligence, vol. 1, no. 5, pp. 30-37, 2012, ISSN: 1989-1660.
BibTeX | Tags:
@article{k517,
title = {A Grammatical Approach to the Modeling of an Autonomous Robot},
author = {G. López-García and A. J. Gallego and J. L. Dalmau-Espert and R. Molina-Carmona and P. Compan-Rosique},
issn = {1989-1660},
year = {2012},
date = {2012-01-01},
journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
volume = {1},
number = {5},
pages = {30-37},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
López-García, G.; Gallego, A. J.; Dalmau-Espert, J. L.; Molina-Carmona, R.; Compan-Rosique, P.
Modeling a Mobile Robot Using a Grammatical Model Proceedings Article
In: Distributed Computing and Artificial Intelligence, pp. 445-452, 2012, ISBN: 978-3-642-28765-7.
BibTeX | Tags:
@inproceedings{k516,
title = {Modeling a Mobile Robot Using a Grammatical Model},
author = {G. López-García and A. J. Gallego and J. L. Dalmau-Espert and R. Molina-Carmona and P. Compan-Rosique},
isbn = {978-3-642-28765-7},
year = {2012},
date = {2012-01-01},
booktitle = {Distributed Computing and Artificial Intelligence},
pages = {445-452},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2011
Rizo, D.; Iñesta, J. M.; Lemström, K.
Polyphonic Music Retrieval with Classifier Ensembles Journal Article
In: Journal of New Music Research, vol. 40, no. 4, pp. 313-324, 2011, ISSN: 0929-8215.
@article{k284,
title = {Polyphonic Music Retrieval with Classifier Ensembles},
author = {D. Rizo and J. M. Iñesta and K. Lemström},
issn = {0929-8215},
year = {2011},
date = {2011-12-01},
urldate = {2011-12-01},
journal = {Journal of New Music Research},
volume = {40},
number = {4},
pages = {313-324},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {article}
}
Iñesta, J. M.; Pérez-García, T.
A Multimodal Music Transcription Prototype Proceedings Article
In: Proc. of International Conference on Multimodal Interaction, ICMI 2011, pp. 315–318, ACM, Alicante, Spain, 2011, ISBN: 978-1-4503-0641-6.
Abstract | BibTeX | Tags: DRIMS, MIPRCV
@inproceedings{k274,
title = {A Multimodal Music Transcription Prototype},
author = {J. M. Iñesta and T. Pérez-García},
isbn = {978-1-4503-0641-6},
year = {2011},
date = {2011-11-01},
urldate = {2011-11-01},
booktitle = {Proc. of International Conference on Multimodal Interaction, ICMI 2011},
pages = {315--318},
publisher = {ACM},
address = {Alicante, Spain},
abstract = {Music transcription consists of transforming an audio signal encoding a music performance in a symbolic representation such as a music score. In this paper, a multimodal and interactive prototype to perform music transcription is
presented. The system is oriented to monotimbral transcription, its working domain is music played by a single instrument. This prototype uses three different sources of information to detect notes in a musical audio excerpt. It has been developed to allow a human expert to interact with the system to improve its results. In its current implementation, it offers a limited range of interaction and multimodality. Further development aimed at full interactivity and multimodal interactions is discussed.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {inproceedings}
}
Music transcription consists of transforming an audio signal encoding a music performance in a symbolic representation such as a music score. In this paper, a multimodal and interactive prototype to perform music transcription is
presented. The system is oriented to monotimbral transcription, its working domain is music played by a single instrument. This prototype uses three different sources of information to detect notes in a musical audio excerpt. It has been developed to allow a human expert to interact with the system to improve its results. In its current implementation, it offers a limited range of interaction and multimodality. Further development aimed at full interactivity and multimodal interactions is discussed. Higuera, C. De La; Oncina, J.
Finding the most probable string and the consensus string: an algorithmic study Proceedings Article
In: In: 12th International Conference on Parsing Technologies (IWPT 2011), pp. 26-36, Dublin, 2011.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k288,
title = {Finding the most probable string and the consensus string: an algorithmic study},
author = {C. De La Higuera and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/288/iwpt2011.pdf},
year = {2011},
date = {2011-10-01},
urldate = {2011-10-01},
booktitle = {In: 12th International Conference on Parsing Technologies (IWPT 2011)},
pages = {26-36},
address = {Dublin},
abstract = {The problem of finding the most probable string for a distribution generated by a weighted finite automaton is related to a number of important questions: computing the distance between two distributions or finding the best translation (the most probable one) given a probabilistic finite state transducer. The problem is undecidable with general weights and is $NP$-hard if the automaton is probabilistic. In this paper we give a pseudo-polynomial algorithm which computes the most probable string in time polynomial in the inverse of the probability of this string itself. We also give a randomised algorithm solving the same problem and discuss the case where the distribution is generated by other types of machines.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
The problem of finding the most probable string for a distribution generated by a weighted finite automaton is related to a number of important questions: computing the distance between two distributions or finding the best translation (the most probable one) given a probabilistic finite state transducer. The problem is undecidable with general weights and is $NP$-hard if the automaton is probabilistic. In this paper we give a pseudo-polynomial algorithm which computes the most probable string in time polynomial in the inverse of the probability of this string itself. We also give a randomised algorithm solving the same problem and discuss the case where the distribution is generated by other types of machines. Miotto, R.; Rizo, D.; Orio, N.; Lartillot, O.
MusiCLEF: a Benchmark Activity in Multimodal Music Information Retrieval Proceedings Article
In: Proc. of the 12th International Society for Music Information Retrieval Conference (ISMIR), Miami 2011, pp. 603-608, University of Miami, 2011, ISBN: 978-0-615-54865-4.
@inproceedings{k275,
title = {MusiCLEF: a Benchmark Activity in Multimodal Music Information Retrieval},
author = {R. Miotto and D. Rizo and N. Orio and O. Lartillot},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/275/Orio_etal_Ismir_2011.pdf},
isbn = {978-0-615-54865-4},
year = {2011},
date = {2011-10-01},
urldate = {2011-10-01},
booktitle = {Proc. of the 12th International Society for Music Information Retrieval Conference (ISMIR), Miami 2011},
pages = {603-608},
publisher = {University of Miami},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {inproceedings}
}
León, Pedro J. Ponce
A statistical pattern recognition approach to symbolic music classification PhD Thesis
2011.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV
@phdthesis{k271,
title = {A statistical pattern recognition approach to symbolic music classification},
author = {Pedro J. Ponce León},
editor = {José M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/271/PhD_Pedro_J_Ponce_de_Leon_2011.pdf},
year = {2011},
date = {2011-09-01},
address = {Alicante, Spain},
organization = {University of Alicante},
abstract = {[ENGLISH] This is a work in the field of Music Information Retrieval, from symbolic sources (digital music scores or similar). It applies statistical pattern recognition techniques to approach two different, but related, problems: melody part selection in polyphonic works, and automatic music genre classification.
[ESPAÑOL] El trabajo se enmarca en el dominio de Recuperación de Música por Ordenador, a partir de fuentes simbólicas (partituras digitales o similares). En concreto, se plantean soluciones computacionales mediante la aplicación de técnicas estadísticas de reconocimiento de formas a dos problemas: la selección automática de partes melódicas en obras polifónicas y la clasificación automática de géneros musicales. Entre las posibles aplicaciones de estas técnicas está la catalogación, indexación y recuperación automática de obras musicales, basadas en su contenido, de grandes bases de datos que contienen obras en formato simbólico (partituras digitales, archivos MIDI, etc.). Otras aplicaciones, en el ámbito de la musicología computacional, incluyen la caracterización de géneros musicales y melodías mediante el análisis automático del contenido de grandes volúmenes de obras musicales.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {phdthesis}
}
[ENGLISH] This is a work in the field of Music Information Retrieval, from symbolic sources (digital music scores or similar). It applies statistical pattern recognition techniques to approach two different, but related, problems: melody part selection in polyphonic works, and automatic music genre classification.
[ESPAÑOL] El trabajo se enmarca en el dominio de Recuperación de Música por Ordenador, a partir de fuentes simbólicas (partituras digitales o similares). En concreto, se plantean soluciones computacionales mediante la aplicación de técnicas estadísticas de reconocimiento de formas a dos problemas: la selección automática de partes melódicas en obras polifónicas y la clasificación automática de géneros musicales. Entre las posibles aplicaciones de estas técnicas está la catalogación, indexación y recuperación automática de obras musicales, basadas en su contenido, de grandes bases de datos que contienen obras en formato simbólico (partituras digitales, archivos MIDI, etc.). Otras aplicaciones, en el ámbito de la musicología computacional, incluyen la caracterización de géneros musicales y melodías mediante el análisis automático del contenido de grandes volúmenes de obras musicales. Socorro, R.; Micó, L.; Oncina, J.
A fast pivot-based indexing algorithm for metric spaces Journal Article
In: Pattern Recognition Letters, vol. 32, no. 11, pp. 1511-1516, 2011.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@article{k266,
title = {A fast pivot-based indexing algorithm for metric spaces},
author = {R. Socorro and L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/266/piaesa-prl.pdf},
year = {2011},
date = {2011-08-01},
urldate = {2011-08-01},
journal = {Pattern Recognition Letters},
volume = {32},
number = {11},
pages = {1511-1516},
abstract = {This work focus on fast nearest neighbor (NN) search algorithms that can work in any metric space (not just the Euclidean distance) and where the distance computation is very time consuming. One of the most well known methods in this field is the AESA algorithm, used as baseline for performance measurement for over twenty years. The AESA works in two steps that repeats: first it searches a promising candidate to NN and computes its distance (approximation step), next it eliminates all the unsuitable NN candidates in view of the new information acquired in the previous calculation (elimination step).
This work introduces the PiAESA algorithm. This algorithm improves the performance of the AESA algorithm by splitting the approximation criterion: on the first iterations, when there is not enough information to find good NN candidates, it uses a list of pivots (objects in the database) to obtain a cheap approximation of the distance function. Once a good approximation is obtained it switches to the AESA usual behavior. As the pivot list is built in preprocessing time, the run time of PiAESA is almost the same than the AESA one.
In this work, we report experiments comparing with some competing methods. Our empirical results show that this new approach obtains a significant reduction of distance computations with no execution time penalty.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
This work focus on fast nearest neighbor (NN) search algorithms that can work in any metric space (not just the Euclidean distance) and where the distance computation is very time consuming. One of the most well known methods in this field is the AESA algorithm, used as baseline for performance measurement for over twenty years. The AESA works in two steps that repeats: first it searches a promising candidate to NN and computes its distance (approximation step), next it eliminates all the unsuitable NN candidates in view of the new information acquired in the previous calculation (elimination step).
This work introduces the PiAESA algorithm. This algorithm improves the performance of the AESA algorithm by splitting the approximation criterion: on the first iterations, when there is not enough information to find good NN candidates, it uses a list of pivots (objects in the database) to obtain a cheap approximation of the distance function. Once a good approximation is obtained it switches to the AESA usual behavior. As the pivot list is built in preprocessing time, the run time of PiAESA is almost the same than the AESA one.
In this work, we report experiments comparing with some competing methods. Our empirical results show that this new approach obtains a significant reduction of distance computations with no execution time penalty. Bernabeu, J. F.; Calera-Rubio, J.; Iñesta, J. M.; Rizo, D.
Melodic Identification Using Probabilistic Tree Automata Journal Article
In: Journal of New Music Research, vol. 40, no. 2, pp. 93-103, 2011, ISSN: 0929-8215.
Abstract | BibTeX | Tags: DRIMS, MIPRCV, TIASA
@article{k270,
title = {Melodic Identification Using Probabilistic Tree Automata},
author = {J. F. Bernabeu and J. Calera-Rubio and J. M. Iñesta and D. Rizo},
issn = {0929-8215},
year = {2011},
date = {2011-06-01},
urldate = {2011-06-01},
journal = {Journal of New Music Research},
volume = {40},
number = {2},
pages = {93-103},
abstract = {Similarity computation is a difficult issue in music information retrieval tasks, because it tries to emulate the special ability that humans show for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labelling has proven to be effective in melodic similarity computation. One of the main drawbacks of this approach is that the tree comparison algorithms are of a high time complexity. In this paper, stochastic k-testable tree-models are applied for computing the similarity between two melodies as a probability. The results are compared to those achieved by tree edit distances, showing that k-testable tree-models outperform other reference methods in both recognition rate and efficiency. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and with efficiency for scalability issues.},
keywords = {DRIMS, MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
Similarity computation is a difficult issue in music information retrieval tasks, because it tries to emulate the special ability that humans show for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labelling has proven to be effective in melodic similarity computation. One of the main drawbacks of this approach is that the tree comparison algorithms are of a high time complexity. In this paper, stochastic k-testable tree-models are applied for computing the similarity between two melodies as a probability. The results are compared to those achieved by tree edit distances, showing that k-testable tree-models outperform other reference methods in both recognition rate and efficiency. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and with efficiency for scalability issues. Oncina, J.; Vidal, E.
Interactive Structured Output Prediction: Application to Chromosome Classification Journal Article
In: Pattern Recognition and Image Analysis (LNCS), vol. 6669, pp. 256-264, 2011.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@article{k267,
title = {Interactive Structured Output Prediction: Application to Chromosome Classification},
author = {J. Oncina and E. Vidal},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/267/karyo.pdf},
year = {2011},
date = {2011-06-01},
urldate = {2011-06-01},
journal = {Pattern Recognition and Image Analysis (LNCS)},
volume = {6669},
pages = {256-264},
abstract = {Interactive Pattern Recognition concepts and techniques are applied to problems with structured output and i.e., problems in which the result is not just a simple class label, but a suitable structure of labels. For illustration purposes (a simplification of) the problem of Human Karyotyping is considered. Results show that a) taking into account label dependencies in a karyogram significantly reduces the classical (noninteractive) chromosome label prediction error rate and b) they are further improved when interactive processing is adopted.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
Interactive Pattern Recognition concepts and techniques are applied to problems with structured output and i.e., problems in which the result is not just a simple class label, but a suitable structure of labels. For illustration purposes (a simplification of) the problem of Human Karyotyping is considered. Results show that a) taking into account label dependencies in a karyogram significantly reduces the classical (noninteractive) chromosome label prediction error rate and b) they are further improved when interactive processing is adopted. Iñesta, J. M.; Pérez-Sancho, C.; Hontanilla, M.
Composer Recognition using Language Models Proceedings Article
In: Proc. of Signal Processing, Pattern Recognition, and Applications (SPPRA 2011), pp. 76-83, ACTA Press, Innsbruck, Austria, 2011, ISBN: 978-0-88986-865-6.
Abstract | BibTeX | Tags: DRIMS, UA-CPS
@inproceedings{k261,
title = {Composer Recognition using Language Models},
author = {J. M. Iñesta and C. Pérez-Sancho and M. Hontanilla},
isbn = {978-0-88986-865-6},
year = {2011},
date = {2011-02-01},
urldate = {2011-02-01},
booktitle = {Proc. of Signal Processing, Pattern Recognition, and Applications (SPPRA 2011)},
pages = {76-83},
publisher = {ACTA Press},
address = {Innsbruck, Austria},
abstract = {In this paper we present an application of language modeling
techniques using n-grams to an authorship attribution
task. An stylometric study has been conducted on a pair
of datasets of baroque and classical composers, with which
other authors performed previously a similar study using a
set of musicological features and pattern recognition techniques.
In this paper, a simple general-purpose encoding
method has been used, in conjunction with language modeling
to explore the same problem. The results show that
this simpler method can lead to the same conclusions than
other more sophisticated methods, even traditional musicological
studies, without the need of advanced musicological
knowledge for processing the scores.},
keywords = {DRIMS, UA-CPS},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we present an application of language modeling
techniques using n-grams to an authorship attribution
task. An stylometric study has been conducted on a pair
of datasets of baroque and classical composers, with which
other authors performed previously a similar study using a
set of musicological features and pattern recognition techniques.
In this paper, a simple general-purpose encoding
method has been used, in conjunction with language modeling
to explore the same problem. The results show that
this simpler method can lead to the same conclusions than
other more sophisticated methods, even traditional musicological
studies, without the need of advanced musicological
knowledge for processing the scores. Socorro, R.; Micó, L.; Oncina, J.
Efficient search supporting several similarity queries by reordering pivots Proceedings Article
In: Signal Processing, Pattern Recognition, and Applications (SPPRA 2011), pp. 114-120, ACTA Press, Innsbruck, Austria, 2011, ISBN: 978-0-88986-865-6.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k260,
title = {Efficient search supporting several similarity queries by reordering pivots},
author = {R. Socorro and L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/260/sppra.pdf},
isbn = {978-0-88986-865-6},
year = {2011},
date = {2011-02-01},
booktitle = {Signal Processing, Pattern Recognition, and Applications (SPPRA 2011)},
pages = {114-120},
publisher = {ACTA Press},
address = {Innsbruck, Austria},
abstract = {Effective similarity search indexing in general metric spaces has traditionally received special attention in several areas of interest like pattern recognition, computer vision or information retrieval. A typical method is based on the use of a distance as a dissimilarity function (not restricting to Euclidean distance) where the main objective is to speed up the search of the most similar object in a database by
minimising the number of distance computations. Several types of search can be defined, being the k-nearest neighbour or the range search the most common. AESA is one of the most well known of such algorithms due to its performance (measured in distance computations). PiAESA is an AESA variant where the main objective has changed. Instead of trying to find the best nearest neighbour candidate at each step, it tries to find the object that contributes the most to have a bigger lower bound function, that is, a better estimation of the distance. In this paper we extend and test PiAESA to support several similarity queries. Our empirical results show that this approach obtains a significant improvement in performance when comparing with competing algorithms.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Effective similarity search indexing in general metric spaces has traditionally received special attention in several areas of interest like pattern recognition, computer vision or information retrieval. A typical method is based on the use of a distance as a dissimilarity function (not restricting to Euclidean distance) where the main objective is to speed up the search of the most similar object in a database by
minimising the number of distance computations. Several types of search can be defined, being the k-nearest neighbour or the range search the most common. AESA is one of the most well known of such algorithms due to its performance (measured in distance computations). PiAESA is an AESA variant where the main objective has changed. Instead of trying to find the best nearest neighbour candidate at each step, it tries to find the object that contributes the most to have a bigger lower bound function, that is, a better estimation of the distance. In this paper we extend and test PiAESA to support several similarity queries. Our empirical results show that this approach obtains a significant improvement in performance when comparing with competing algorithms. Oncina, J.; Rodríguez, R.
Interactive Text Generation Book Chapter
In: Toselli, A.; Vidal, E.; Casacuberta, F. (Ed.): Multimodal Interactive Pattern Recognition and Applications, Chapter 10, pp. 195-207, Springer, 2011, ISBN: 978-0-85729-478-4.
BibTeX | Tags: MIPRCV, PASCAL2
@inbook{k291,
title = {Interactive Text Generation},
author = {J. Oncina and R. Rodríguez},
editor = {A. Toselli and E. Vidal and F. Casacuberta},
isbn = {978-0-85729-478-4},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {Multimodal Interactive Pattern Recognition and Applications},
pages = {195-207},
publisher = {Springer},
chapter = {10},
keywords = {MIPRCV, PASCAL2},
pubstate = {published},
tppubtype = {inbook}
}
Iñesta, J. M.; Rizo, D.; Illescas, P. R.
Learning melodic analysis rules Technical Report
2011.
Abstract | Links | BibTeX | Tags: DRIMS
@techreport{k276,
title = {Learning melodic analysis rules},
author = {J. M. Iñesta and D. Rizo and P. R. Illescas},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/276/mml2011-melan-final.pdf},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {4th Int.Workshop on Music and Machine Learning},
organization = {NIPS},
abstract = {Automatic musical analysis has been approached from different perspectives: grammars, expert systems, probabilistic models, and model matching have been proposed for implementing tonal analysis. In this work we focus on automatic melodic analysis. One question that arises when building a melodic analysis system using a-priori music theory is whether it is possible to automatically extract analysis rules from examples, and how similar are those learnt rules compared to music theory rules. This work investigates this question, i.e. given a dataset of analyzed melodies our objective is to automatically learn analysis rules and to compare them with music theory rules.},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {techreport}
}
Automatic musical analysis has been approached from different perspectives: grammars, expert systems, probabilistic models, and model matching have been proposed for implementing tonal analysis. In this work we focus on automatic melodic analysis. One question that arises when building a melodic analysis system using a-priori music theory is whether it is possible to automatically extract analysis rules from examples, and how similar are those learnt rules compared to music theory rules. This work investigates this question, i.e. given a dataset of analyzed melodies our objective is to automatically learn analysis rules and to compare them with music theory rules. Serrano, A.; Micó, L.; Oncina, J.
Impact of the Initialization in Tree-Based Fast Similarity Search Techniques Proceedings Article
In: Pelillo, M.; Hancock, E. R. (Ed.): SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition, pp. 163-176, Springer, Venecia, Italia, 2011, ISBN: 978-3-642-24470-4.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k272,
title = {Impact of the Initialization in Tree-Based Fast Similarity Search Techniques},
author = {A. Serrano and L. Micó and J. Oncina},
editor = {M. Pelillo and E. R. Hancock},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/272/simbad11.pdf},
isbn = {978-3-642-24470-4},
year = {2011},
date = {2011-01-01},
booktitle = {SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition},
pages = {163-176},
publisher = {Springer},
address = {Venecia, Italia},
abstract = {Many fast similarity search techniques relies on the use of pivots (specially selected points in the data set). Using these points, specific structures (indexes) are built speeding up the search when queering. Usually, pivot selection techniques are incremental, being the first one randomly chosen.
This article explores several techniques to choose the first pivot in a tree-based fast similarity search technique. We provide experimental results showing that an adequate choice of this pivot leads to significant reductions in distance computations and time complexity.
Moreover, most pivot tree-based indexes emphasizes in building balanced trees.We provide experimentally and theoretical support that very unbalanced trees can be a better choice than balanced ones.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Many fast similarity search techniques relies on the use of pivots (specially selected points in the data set). Using these points, specific structures (indexes) are built speeding up the search when queering. Usually, pivot selection techniques are incremental, being the first one randomly chosen.
This article explores several techniques to choose the first pivot in a tree-based fast similarity search technique. We provide experimental results showing that an adequate choice of this pivot leads to significant reductions in distance computations and time complexity.
Moreover, most pivot tree-based indexes emphasizes in building balanced trees.We provide experimentally and theoretical support that very unbalanced trees can be a better choice than balanced ones. Calvo-Zaragoza, J.; Rizo, D.; Iñesta, J. M.
A distance for partially labeled trees Journal Article
In: Lecture Notes in Computer Science, vol. 6669, pp. 492–499, 2011, ISSN: 0302-9743.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV
@article{k265,
title = {A distance for partially labeled trees},
author = {J. Calvo-Zaragoza and D. Rizo and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/265/ibpria11-calvo.pdf},
issn = {0302-9743},
year = {2011},
date = {2011-01-01},
journal = {Lecture Notes in Computer Science},
volume = {6669},
pages = {492--499},
abstract = {Trees are a powerful data structure for representing data for which hierarchical
relations can be defined. It has been applied in a number of fields like
image analysis, natural language processing, protein structure, or music
retrieval, to name a few. Procedures for comparing trees are very relevant
in many tasks where tree representations are involved. The computation of
these measures is usually time consuming and different authors have
proposed algorithms that are able to compute them in a reasonable time,
by means of approximated versions of the similarity measure. Other methods
require that the trees are fully labeled for the distance to be computed.
The measure utilized in this paper is able to deal with trees labeled
only at the leaves that runs in $O(|T_1|times|T_2|)$ time. Experiments and
comparative results are provided.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Trees are a powerful data structure for representing data for which hierarchical
relations can be defined. It has been applied in a number of fields like
image analysis, natural language processing, protein structure, or music
retrieval, to name a few. Procedures for comparing trees are very relevant
in many tasks where tree representations are involved. The computation of
these measures is usually time consuming and different authors have
proposed algorithms that are able to compute them in a reasonable time,
by means of approximated versions of the similarity measure. Other methods
require that the trees are fully labeled for the distance to be computed.
The measure utilized in this paper is able to deal with trees labeled
only at the leaves that runs in $O(|T_1|times|T_2|)$ time. Experiments and
comparative results are provided. Bernabeu, J. F.; Calera-Rubio, J.; Iñesta, J. M.
Classifying melodies using tree grammars Journal Article
In: Lecture Notes in Computer Science, vol. 6669, pp. 572–579, 2011, ISSN: 0302-9743.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV, TIASA
@article{k264,
title = {Classifying melodies using tree grammars},
author = {J. F. Bernabeu and J. Calera-Rubio and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/264/ibpria2011-bernabeu.pdf},
issn = {0302-9743},
year = {2011},
date = {2011-01-01},
journal = {Lecture Notes in Computer Science},
volume = {6669},
pages = {572--579},
abstract = {Similarity computation is a difficult issue in music information retrieval, because it tries to emulate the special ability that humans show
for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what
is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labeling has proven to be effective in melodic similarity computation. In this paper we propose a solution when we have melodies represented by trees for the training but the duration information is not available for the input data. For that, we infer a probabilistic context-free grammar using the information in the trees (duration and pitch) and classify new melodies represented by strings using only the pitch. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and efficient for scalability issues.},
keywords = {DRIMS, MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
Similarity computation is a difficult issue in music information retrieval, because it tries to emulate the special ability that humans show
for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what
is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labeling has proven to be effective in melodic similarity computation. In this paper we propose a solution when we have melodies represented by trees for the training but the duration information is not available for the input data. For that, we infer a probabilistic context-free grammar using the information in the trees (duration and pitch) and classify new melodies represented by strings using only the pitch. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and efficient for scalability issues. Abreu, J.; Rico-Juan, J. R.
Characterization of contour regularities based on the Levenshtein edit distance Journal Article
In: Pattern Recognition Letters, vol. 32, pp. 1421-1427, 2011.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@article{k263,
title = {Characterization of contour regularities based on the Levenshtein edit distance},
author = {J. Abreu and J. R. Rico-Juan},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/263/2009_J_IbPRIA.pdf},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
journal = {Pattern Recognition Letters},
volume = {32},
pages = {1421-1427},
abstract = {This paper describes a new method for quantifying the regularity of contours and comparing them (when encoded by Freeman chain codes) in terms of a similarity criterion which relies on information gathered from Levenshtein edit distance computation. The criterion used allows subsequences to be found from the minimal cost edit sequence that specifies an alignment of contour segments which are similar. Two external parameters adjust the similarity criterion. The information about each similar part is encoded by strings that represent an average contour region. An explanation of how to construct a prototype based on the identified regularities is also reviewed. The reliability of the prototypes is evaluated by replacing contour groups (samples) by new prototypes used as the training set in a classification task. This way, the size of the data set can be reduced without sensibly affecting its representational power for classification purposes. Experimental results show that this scheme achieves a reduction in the size of the training data set of about 80% while the classification error only increases by 0.45% in one of the three data sets studied.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
This paper describes a new method for quantifying the regularity of contours and comparing them (when encoded by Freeman chain codes) in terms of a similarity criterion which relies on information gathered from Levenshtein edit distance computation. The criterion used allows subsequences to be found from the minimal cost edit sequence that specifies an alignment of contour segments which are similar. Two external parameters adjust the similarity criterion. The information about each similar part is encoded by strings that represent an average contour region. An explanation of how to construct a prototype based on the identified regularities is also reviewed. The reliability of the prototypes is evaluated by replacing contour groups (samples) by new prototypes used as the training set in a classification task. This way, the size of the data set can be reduced without sensibly affecting its representational power for classification purposes. Experimental results show that this scheme achieves a reduction in the size of the training data set of about 80% while the classification error only increases by 0.45% in one of the three data sets studied.2010
Rizo, D.
Symbolic music comparison with tree data structures PhD Thesis
2010.
@phdthesis{k258,
title = {Symbolic music comparison with tree data structures},
author = {D. Rizo},
editor = {J. M. Supervisor: Iñesta},
year = {2010},
date = {2010-11-01},
organization = {Universidad de Alicante},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {phdthesis}
}
Ramírez, R.; Conklin, D.; Anagnostopoulou, C.; Iñesta, J. M.
MML 2010: International Workshop on Machine Learning and Music Proceedings Article
In: Proceedings of the international conference on Multimedia, pp. 1733–1734, ACM ACM, Firenze, Italy, 2010, ISBN: 978-1-60558-933-6.
@inproceedings{k259,
title = {MML 2010: International Workshop on Machine Learning and Music},
author = {R. Ramírez and D. Conklin and C. Anagnostopoulou and J. M. Iñesta},
isbn = {978-1-60558-933-6},
year = {2010},
date = {2010-10-01},
urldate = {2010-10-01},
booktitle = {Proceedings of the international conference on Multimedia},
pages = {1733--1734},
publisher = {ACM},
address = {Firenze, Italy},
organization = {ACM},
abstract = {MML 2010, the International Workshop on Machine Learning and Music, continues a series of workshops related to artificial intelligence and machine learning in music. In this short article the Programme Chairs summarize the content of the workshop.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
MML 2010, the International Workshop on Machine Learning and Music, continues a series of workshops related to artificial intelligence and machine learning in music. In this short article the Programme Chairs summarize the content of the workshop. Pérez-García, Pérez-Sancho T.
Harmonic and Instrumental Information Fusion for Musical Genre Classification Proceedings Article
In: Proc. of. ACM Multimedia Workshop on Music and Machine Learning (MML 2010), pp. 49–52, ACM, Florence (Italy), 2010, ISBN: 978-1-60558-933-6.
Abstract | BibTeX | Tags: DRIMS, MIPRCV
@inproceedings{k256,
title = {Harmonic and Instrumental Information Fusion for Musical Genre Classification},
author = {Pérez-Sancho T. Pérez-García},
isbn = {978-1-60558-933-6},
year = {2010},
date = {2010-10-01},
booktitle = {Proc. of. ACM Multimedia Workshop on Music and Machine Learning (MML 2010)},
pages = {49--52},
publisher = {ACM},
address = {Florence (Italy)},
abstract = {This paper presents a musical genre classification system
based on the combination of two kinds of information of very
different nature: the instrumentation information contained
in a MIDI file (metadata) and the chords that provide the
harmonic structure of the musical score stored in that file
(content). The fusion of these two information sources gives
a single feature vector that represents the file and to which
classification techniques usually utilized for text categorization
tasks are applied. The classification task is performed
under a probabilistic approach that has improved the results
previously obtained for the same data using the instrumental
or the chord information independently.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a musical genre classification system
based on the combination of two kinds of information of very
different nature: the instrumentation information contained
in a MIDI file (metadata) and the chords that provide the
harmonic structure of the musical score stored in that file
(content). The fusion of these two information sources gives
a single feature vector that represents the file and to which
classification techniques usually utilized for text categorization
tasks are applied. The classification task is performed
under a probabilistic approach that has improved the results
previously obtained for the same data using the instrumental
or the chord information independently. Rauber, A.; Mayer, R.
Feature Selection in a Cartesian Ensemble of Feature Subspace Classifiers for Music Categorisation Proceedings Article
In: Proc. of. ACM Multimedia Workshop on Music and Machine Learning (MML 2010), pp. 53–56, ACM, Florence (Italy), 2010, ISBN: 978-1-60558-933-6.
Abstract | BibTeX | Tags: DRIMS, MIPRCV
@inproceedings{k255,
title = {Feature Selection in a Cartesian Ensemble of Feature Subspace Classifiers for Music Categorisation},
author = {A. Rauber and R. Mayer},
isbn = {978-1-60558-933-6},
year = {2010},
date = {2010-10-01},
urldate = {2010-10-01},
booktitle = {Proc. of. ACM Multimedia Workshop on Music and Machine Learning (MML 2010)},
pages = {53--56},
publisher = {ACM},
address = {Florence (Italy)},
abstract = {We evaluate the impact of feature selection on the classification
accuracy and the achieved dimensionality reduction,
which benefits the time needed on training classification
models. Our classification scheme therein is a Cartesian en-
semble classification system, based on the principle of late
fusion and feature subspaces. These feature subspaces describe
different aspects of the same data set. We use it for
the ensemble classification of multiple feature sets from the
audio and symbolic domains. We present an extensive set
of experiments in the context of music genre classification,
based on Music IR benchmark datasets. We show that while
feature selection does not benefit classification accuracy, it
greatly reduces the dimensionality of each feature subspace,
and thus adds to great gains in the time needed to train the
individual classification models that form the ensemble.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {inproceedings}
}
We evaluate the impact of feature selection on the classification
accuracy and the achieved dimensionality reduction,
which benefits the time needed on training classification
models. Our classification scheme therein is a Cartesian en-
semble classification system, based on the principle of late
fusion and feature subspaces. These feature subspaces describe
different aspects of the same data set. We use it for
the ensemble classification of multiple feature sets from the
audio and symbolic domains. We present an extensive set
of experiments in the context of music genre classification,
based on Music IR benchmark datasets. We show that while
feature selection does not benefit classification accuracy, it
greatly reduces the dimensionality of each feature subspace,
and thus adds to great gains in the time needed to train the
individual classification models that form the ensemble. Pérez, A.; Ramírez, R.; Iñesta, J. M.
Modeling violin performances using inductive logic programming Journal Article
In: Intelligent Data Analysis, vol. 14, no. 5, pp. 573–585, 2010, ISSN: 1088-467X.
@article{k253,
title = {Modeling violin performances using inductive logic programming},
author = {A. Pérez and R. Ramírez and J. M. Iñesta},
issn = {1088-467X},
year = {2010},
date = {2010-09-01},
urldate = {2010-09-01},
journal = {Intelligent Data Analysis},
volume = {14},
number = {5},
pages = {573--585},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {article}
}
Iñesta, J. M.; Pérez-Sancho, C.; Pérez-García, T.
Fusión de información armónica e instrumental para la clasificación de géneros musicales Proceedings Article
In: Pérez, Juan Carlos (Ed.): Actas del II Workshop de Reconocimiento de Formas y Análisis de Imágenes (AERFAI), pp. 147-153, AERFAI Ibergarceta Publicaciones S.L., Valencia, Spain, 2010, ISBN: 978-84-92812-66-0.
Abstract | BibTeX | Tags: DRIMS, MIPRCV
@inproceedings{k252,
title = {Fusión de información armónica e instrumental para la clasificación de géneros musicales},
author = {J. M. Iñesta and C. Pérez-Sancho and T. Pérez-García},
editor = {Juan Carlos Pérez},
isbn = {978-84-92812-66-0},
year = {2010},
date = {2010-09-01},
urldate = {2010-09-01},
booktitle = {Actas del II Workshop de Reconocimiento de Formas y Análisis de Imágenes (AERFAI)},
pages = {147-153},
publisher = {Ibergarceta Publicaciones S.L.},
address = {Valencia, Spain},
organization = {AERFAI},
abstract = {En este artículo presentamos un sistema de clasificación de género musical basado en la combinación de dos tipos diferentes de información: la información instrumental contenida en un fichero MIDI y los acordes que proporcionan la estructura armónica de la partitura musical almacenada en dicho fichero. La unión de estas informaciones nos proporciona un único vector de caracteríticas sobre el que se aplican técnicas usadas habitualmente en la clasificación de textos. Finalmente esto nos proporciona un clasificador probabilítico que mejora los resultados obtenidos en trabajos previos en los que se usaba de forma independiente la información instrumental y la información armónica de un fichero MIDI.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {inproceedings}
}
En este artículo presentamos un sistema de clasificación de género musical basado en la combinación de dos tipos diferentes de información: la información instrumental contenida en un fichero MIDI y los acordes que proporcionan la estructura armónica de la partitura musical almacenada en dicho fichero. La unión de estas informaciones nos proporciona un único vector de caracteríticas sobre el que se aplican técnicas usadas habitualmente en la clasificación de textos. Finalmente esto nos proporciona un clasificador probabilítico que mejora los resultados obtenidos en trabajos previos en los que se usaba de forma independiente la información instrumental y la información armónica de un fichero MIDI. Calera-Rubio, J.; Bernabeu, J. F.
Tree language automata for melody recognition Proceedings Article
In: Pérez, Juan Carlos (Ed.): Actas del II Workshop de Reconocimiento de Formas y Análisis de Imágenes (AERFAI), pp. 17-22, AERFAI IBERGARCETA PUBLICACIONES, S.L., Valencia, Spain, 2010, ISBN: 978-84-92812-66-0.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV, TIASA
@inproceedings{k251,
title = {Tree language automata for melody recognition},
author = {J. Calera-Rubio and J. F. Bernabeu},
editor = {Juan Carlos Pérez},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/251/bernabeuCEDI2010Final.pdf},
isbn = {978-84-92812-66-0},
year = {2010},
date = {2010-09-01},
urldate = {2010-09-01},
booktitle = {Actas del II Workshop de Reconocimiento de Formas y Análisis de Imágenes (AERFAI)},
pages = {17-22},
publisher = {IBERGARCETA PUBLICACIONES, S.L.},
address = {Valencia, Spain},
organization = {AERFAI},
abstract = {The representation of symbolic music by
means of trees has shown to be suitable in
melodic similarity computation. In order to
compare trees, different tree edit distances
have been previously used, being their complexity
a main drawback. In this paper, the application of stochastic k-testable treemodels for computing the similarity between two melodies as a probability, compared to the classical edit distance has been addressed. The results show that k-testable tree-models seem to be adequate for the task, since they outperform other reference methods in both recognition rate and efficiency. The case study in this paper is to identify a snippet query among a set of songs. For it, the utilized method must be able to deal with inexact queries and efficiency
for scalability issues.},
keywords = {DRIMS, MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
The representation of symbolic music by
means of trees has shown to be suitable in
melodic similarity computation. In order to
compare trees, different tree edit distances
have been previously used, being their complexity
a main drawback. In this paper, the application of stochastic k-testable treemodels for computing the similarity between two melodies as a probability, compared to the classical edit distance has been addressed. The results show that k-testable tree-models seem to be adequate for the task, since they outperform other reference methods in both recognition rate and efficiency. The case study in this paper is to identify a snippet query among a set of songs. For it, the utilized method must be able to deal with inexact queries and efficiency
for scalability issues. Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.; León, P. J. Ponce; Kersten, S.; Ramírez, R.
Genre classification of music by tonal harmony Journal Article
In: Intelligent Data Analysis, vol. 14, no. 5, pp. 533-545, 2010, ISSN: 1088-467X.
Abstract | BibTeX | Tags: Acc. Int. E-A, DRIMS, PROSEMUS
@article{k232,
title = {Genre classification of music by tonal harmony},
author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta and P. J. Ponce León and S. Kersten and R. Ramírez},
issn = {1088-467X},
year = {2010},
date = {2010-09-01},
urldate = {2010-09-01},
journal = {Intelligent Data Analysis},
volume = {14},
number = {5},
pages = {533-545},
abstract = {In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task.},
keywords = {Acc. Int. E-A, DRIMS, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task. Micó, L.; Oncina, J.
A Constant Average Time Algorithm to Allow Insertions in the LAESA Fast Nearest Neighbour Search Index Proceedings Article
In: Proc. of the 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey, pp. 23–26, 2010.
Links | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k257,
title = {A Constant Average Time Algorithm to Allow Insertions in the LAESA Fast Nearest Neighbour Search Index},
author = {L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/257/icpr-2010.pdf},
year = {2010},
date = {2010-08-01},
booktitle = {Proc. of the 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey},
pages = {23--26},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Gómez-Ballester, E.; Micó, L.; Thollard, F.; Oncina, J.; Moreno-Seco, F.
Combining Elimination Rules in Tree-Based Nearest Neighbor Search Algorithms Proceedings Article
In: Hancok, E. R.; Wilson, R. C.; Ilkay, T. W.; Escolano, F. (Ed.): Structural, Syntactic, and Statistical Pattern Recognition, pp. 80–89, Springer, Cesme, Turkey, 2010, ISBN: 978-3-642-14979-5.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k249,
title = {Combining Elimination Rules in Tree-Based Nearest Neighbor Search Algorithms},
author = {E. Gómez-Ballester and L. Micó and F. Thollard and J. Oncina and F. Moreno-Seco},
editor = {E. R. Hancok and R. C. Wilson and T. W. Ilkay and F. Escolano},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/249/tr-ssspr2010.pdf},
isbn = {978-3-642-14979-5},
year = {2010},
date = {2010-08-01},
booktitle = {Structural, Syntactic, and Statistical Pattern Recognition},
pages = {80--89},
publisher = {Springer},
address = {Cesme, Turkey},
abstract = {A common activity in many pattern recognition tasks, image processing or clustering techniques involves searching a labeled data set looking for the nearest point to a given unlabelled sample. To reduce the computational overhead when the naive exhaustive search is applied, some fast nearest neighbor search (NNS) algorithms have appeared in the last years. Depending on the structure used to store the training set (usually a tree), different strategies to speed up the search have been defined. In this paper, a new algorithm based on the combination of different pruning rules is proposed. An experimental evaluation and comparison of its behavior with respect to other techniques has been performed, using both real and artificial data.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
A common activity in many pattern recognition tasks, image processing or clustering techniques involves searching a labeled data set looking for the nearest point to a given unlabelled sample. To reduce the computational overhead when the naive exhaustive search is applied, some fast nearest neighbor search (NNS) algorithms have appeared in the last years. Depending on the structure used to store the training set (usually a tree), different strategies to speed up the search have been defined. In this paper, a new algorithm based on the combination of different pruning rules is proposed. An experimental evaluation and comparison of its behavior with respect to other techniques has been performed, using both real and artificial data. Rizo, D.; Iñesta, J. M.
New partially labelled tree similarity measure: a case study Proceedings Article
In: Hancok, E. R.; Wilson, R. C.; Ilkay, T. W.; Escolano, F. (Ed.): Structural, Syntactic, and Statistical Pattern Recognition, pp. 296–305, Springer, Cesme, Turkey, 2010, ISBN: 978-3-642-14979-5.
@inproceedings{k248,
title = {New partially labelled tree similarity measure: a case study},
author = {D. Rizo and J. M. Iñesta},
editor = {E. R. Hancok and R. C. Wilson and T. W. Ilkay and F. Escolano},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/248/ssspr10-cr.pdf},
isbn = {978-3-642-14979-5},
year = {2010},
date = {2010-08-01},
booktitle = {Structural, Syntactic, and Statistical Pattern Recognition},
pages = {296--305},
publisher = {Springer},
address = {Cesme, Turkey},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {inproceedings}
}
Rico-Juan, J. R.; Abreu, J.
A new editing scheme based on a fast two-string median computation applied to OCR Proceedings Article
In: Hancok, E. R.; Wilson, R. C.; Ilkay, T. W.; Escolano, F. (Ed.): Structural, Syntactic, and Statistical Pattern Recognition, pp. 748–756, Springer, Cesme, Izmir, Turkey, 2010, ISBN: 978-3-642-14979-5.
Abstract | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k247,
title = {A new editing scheme based on a fast two-string median computation applied to OCR},
author = {J. R. Rico-Juan and J. Abreu},
editor = {E. R. Hancok and R. C. Wilson and T. W. Ilkay and F. Escolano},
isbn = {978-3-642-14979-5},
year = {2010},
date = {2010-08-01},
urldate = {2010-08-01},
booktitle = {Structural, Syntactic, and Statistical Pattern Recognition},
pages = {748--756},
publisher = {Springer},
address = {Cesme, Izmir, Turkey},
abstract = {This paper presents a new fast algorithm to compute an approximation to the median between two strings of characters representing a 2D shape and its application to a new classification scheme to decrease its error rate. The median string results from the application of certain edit operations from the minimum cost edit sequence to one of the original strings. The new dataset editing scheme relaxes the criterion to delete instances proposed by the Wilson Editing Proce- dure. In practice, not all instances misclassified by its near neighbors are pruned. Instead, an artificial instance is added to the dataset expecting to successfully classify the instance on the future. The new artificial instance is the median from the misclassified sample and its same-class nearest neighbor. The experiments over two widely used datasets of handwritten characters show this preprocessing scheme can reduce the classification error in about 78% of trials.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a new fast algorithm to compute an approximation to the median between two strings of characters representing a 2D shape and its application to a new classification scheme to decrease its error rate. The median string results from the application of certain edit operations from the minimum cost edit sequence to one of the original strings. The new dataset editing scheme relaxes the criterion to delete instances proposed by the Wilson Editing Proce- dure. In practice, not all instances misclassified by its near neighbors are pruned. Instead, an artificial instance is added to the dataset expecting to successfully classify the instance on the future. The new artificial instance is the median from the misclassified sample and its same-class nearest neighbor. The experiments over two widely used datasets of handwritten characters show this preprocessing scheme can reduce the classification error in about 78% of trials. Lidy, T.; Mayer, R.; Rauber, A.; de León, P. J. Ponce; Pertusa, A.; Iñesta, J. M.
A Cartesian Ensemble of Feature Subspace Classifiers for Music Categorization Proceedings Article
In: Downie, J. Stephen; Veltkamp, Remco C. (Ed.): Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), pp. 279-284, International Society for Music Information Retrieval International Society for Music Information Retrieval, Utrecht, Netherlands, 2010, ISBN: 978-90-393-53813.
Abstract | Links | BibTeX | Tags: DRIMS
@inproceedings{k246,
title = {A Cartesian Ensemble of Feature Subspace Classifiers for Music Categorization},
author = {T. Lidy and R. Mayer and A. Rauber and P. J. Ponce de León and A. Pertusa and J. M. Iñesta},
editor = {J. Stephen Downie and Remco C. Veltkamp},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/246/ismir2010.pdf},
isbn = {978-90-393-53813},
year = {2010},
date = {2010-08-01},
urldate = {2010-08-01},
booktitle = {Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010)},
pages = {279-284},
publisher = {International Society for Music Information Retrieval},
address = {Utrecht, Netherlands},
organization = {International Society for Music Information Retrieval},
abstract = {We present a cartesian ensemble classification system that is based on the principle of late fusion and feature subspaces. These feature subspaces describe different aspects of the same data set. The framework is built on the Weka machine learning toolkit and able to combine arbitrary feature sets and learning schemes. In our scenario, we use it for the ensemble classification of multiple feature sets from the audio and symbolic domains. We present an extensive set of experiments in the context of music genre classification, based on numerous Music IR benchmark datasets, and evaluate a set of combination/voting rules. The results show that the approach is superior to the best choice of a single algorithm on a single feature set. Moreover, it also releases the user from making this choice explicitly.},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a cartesian ensemble classification system that is based on the principle of late fusion and feature subspaces. These feature subspaces describe different aspects of the same data set. The framework is built on the Weka machine learning toolkit and able to combine arbitrary feature sets and learning schemes. In our scenario, we use it for the ensemble classification of multiple feature sets from the audio and symbolic domains. We present an extensive set of experiments in the context of music genre classification, based on numerous Music IR benchmark datasets, and evaluate a set of combination/voting rules. The results show that the approach is superior to the best choice of a single algorithm on a single feature set. Moreover, it also releases the user from making this choice explicitly. Verdú-Mas, J. L.
Gramáticas probabilisticas para la desambiguación sintáctica PhD Thesis
2010.
@phdthesis{k262,
title = {Gramáticas probabilisticas para la desambiguación sintáctica},
author = {J. L. Verdú-Mas},
editor = {Jorge Calera Rafael Carrasco},
year = {2010},
date = {2010-01-01},
organization = {Univ. Alicante},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {phdthesis}
}
Iñesta, J. M.; Rizo, D.
Trees and combined methods for monophonic music similarity evaluation Proceedings Article
In: MIREX 2010 - Music Information Retrieval Evaluation eXchange, MIREX Symbolic Melodic Similarity contest, Utrecht, The Nederlands, 2010.
@inproceedings{k254,
title = {Trees and combined methods for monophonic music similarity evaluation},
author = {J. M. Iñesta and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/254/trees.pdf},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
booktitle = {MIREX 2010 - Music Information Retrieval Evaluation eXchange, MIREX Symbolic Melodic Similarity contest},
address = {Utrecht, The Nederlands},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {inproceedings}
}
Pertusa, A.
Computationally efficient methods for polyphonic music transcription PhD Thesis
2010.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV
@phdthesis{k244,
title = {Computationally efficient methods for polyphonic music transcription},
author = {A. Pertusa},
editor = {José M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/244/pertusaphd.pdf},
year = {2010},
date = {2010-01-01},
organization = {Universidad de Alicante},
abstract = {Automatic music transcription is a music information retrieval (MIR) task which involves many different disciplines, such as audio signal processing, machine learning, computer science, psychoacoustics and music perception, music theory, and music cognition. The goal of automatic music transcription is to extract a human readable and interpretable representation, like a musical score, from an audio signal. To achieve this goal, it is necessary to estimate the pitches, onset times and durations of the notes, the tempo, the meter and the tonality of a musical piece.
The most obvious application of automatic music transcription is to help a musician to write down the music notation of a performance from an audio recording, which is a time consuming task when it is done by hand. Besides this application, automatic music transcription can also be useful for other MIR tasks, like plagiarism detection, artist identification, genre classification, and composition assistance by changing the instrumentation, the arrangement or the loudness before resynthesizing new pieces. In general, music transcription methods can also provide information about the notes to symbolic music algorithms.
This work addresses the automatic music transcription problem using different strategies. Novel efficient methods are proposed for onset detection (detection of the beginnings of musical events) and multiple fundamental frequency estimation (estimation of the pitches in a polyphonic mixture), using supervised learning and signal processing techniques.
The main contributions of this work can be summarized in the following points:
- An analytical and extensive review of the state of the art methods for onset detection and multiple fundamental frequency estimation.
- The development of an efficient approach for onset detection and the construction of a public ground-truth data set for this task.
- Two novel approaches for multiple pitch estimation of a priori known sounds using supervised learning methods. These algorithms were one of the first machine learning methods proposed for this task.
- A simple iterative cancellation approach, mainly intended to transcribe piano music at a low computational cost.
- Heuristic multiple fundamental frequency algorithms based on signal processing to analyze real music without any a priori knowledge. These methods, which are probably the main contribution of this work, experimentally reached the state of the art for this task with a very low
computational burden.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {phdthesis}
}
Automatic music transcription is a music information retrieval (MIR) task which involves many different disciplines, such as audio signal processing, machine learning, computer science, psychoacoustics and music perception, music theory, and music cognition. The goal of automatic music transcription is to extract a human readable and interpretable representation, like a musical score, from an audio signal. To achieve this goal, it is necessary to estimate the pitches, onset times and durations of the notes, the tempo, the meter and the tonality of a musical piece.
The most obvious application of automatic music transcription is to help a musician to write down the music notation of a performance from an audio recording, which is a time consuming task when it is done by hand. Besides this application, automatic music transcription can also be useful for other MIR tasks, like plagiarism detection, artist identification, genre classification, and composition assistance by changing the instrumentation, the arrangement or the loudness before resynthesizing new pieces. In general, music transcription methods can also provide information about the notes to symbolic music algorithms.
This work addresses the automatic music transcription problem using different strategies. Novel efficient methods are proposed for onset detection (detection of the beginnings of musical events) and multiple fundamental frequency estimation (estimation of the pitches in a polyphonic mixture), using supervised learning and signal processing techniques.
The main contributions of this work can be summarized in the following points:
- An analytical and extensive review of the state of the art methods for onset detection and multiple fundamental frequency estimation.
- The development of an efficient approach for onset detection and the construction of a public ground-truth data set for this task.
- Two novel approaches for multiple pitch estimation of a priori known sounds using supervised learning methods. These algorithms were one of the first machine learning methods proposed for this task.
- A simple iterative cancellation approach, mainly intended to transcribe piano music at a low computational cost.
- Heuristic multiple fundamental frequency algorithms based on signal processing to analyze real music without any a priori knowledge. These methods, which are probably the main contribution of this work, experimentally reached the state of the art for this task with a very low
computational burden. López-García, G.; Molina-Carmona, A. J. R. Gallego
Formal Model to Integrate Multi-Agent Systems and Interactive Graphic Systems Proceedings Article
In: Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART), pp. 264-267, 2010, ISBN: 978-989-674-022-1.
BibTeX | Tags:
@inproceedings{k518,
title = {Formal Model to Integrate Multi-Agent Systems and Interactive Graphic Systems},
author = {G. López-García and A. J. R. Gallego Molina-Carmona},
isbn = {978-989-674-022-1},
year = {2010},
date = {2010-01-01},
booktitle = {Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART)},
pages = {264-267},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2009
Lidy, T.; Grecu, A.; Rauber, A.; Pertusa, A.; de León, P. J. Ponce; Iñesta, J. M.
A Multi-Feature-Set Multi-Classifier Ensemble Approach For Audio Music Classification Proceedings Article
In: Music Information Retrieval Evaluation eXchange (MIREX 2009), International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL) Kobe, Japan, 2009.
@inproceedings{k243,
title = {A Multi-Feature-Set Multi-Classifier Ensemble Approach For Audio Music Classification},
author = {T. Lidy and A. Grecu and A. Rauber and A. Pertusa and P. J. Ponce de León and J. M. Iñesta},
year = {2009},
date = {2009-10-01},
urldate = {2009-10-01},
booktitle = {Music Information Retrieval Evaluation eXchange (MIREX 2009)},
address = {Kobe, Japan},
organization = {International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL)},
abstract = {The approach of combining a multitude of audio features and also symbolic features (through transcription of audio to MIDI) for music classification proved useful, as shown previously. We extended the system submitted to MIREX 2008 by including temporal audio features, adding another audio analysis algorithm based on finding templates on music, enhancing the polyphonic audio to MIDI transcription system and using an ensemble of classification models specializing on feature subsets, rather than combining all features to feed a single classifier, like in the previous MIREX.
Recent research in music genre classification hints at a glass ceiling being reached using timbral audio features.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The approach of combining a multitude of audio features and also symbolic features (through transcription of audio to MIDI) for music classification proved useful, as shown previously. We extended the system submitted to MIREX 2008 by including temporal audio features, adding another audio analysis algorithm based on finding templates on music, enhancing the polyphonic audio to MIDI transcription system and using an ensemble of classification models specializing on feature subsets, rather than combining all features to feed a single classifier, like in the previous MIREX.
Recent research in music genre classification hints at a glass ceiling being reached using timbral audio features. Iñesta, J. M.; Pérez-García, T.; Rizo, D.
metamidi: a tool for automatic metadata extraction from MIDI files Proceedings Article
In: Rauber, A.; Orio, N.; Rizo, D. (Ed.): Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009, pp. 36–40, Corfu, Greece, 2009, ISBN: 978-84-692-6082-1.
Abstract | Links | BibTeX | Tags: PROSEMUS
@inproceedings{k234,
title = {metamidi: a tool for automatic metadata extraction from MIDI files},
author = {J. M. Iñesta and T. Pérez-García and D. Rizo},
editor = {A. Rauber and N. Orio and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/234/metamidi.pdf},
isbn = {978-84-692-6082-1},
year = {2009},
date = {2009-10-01},
urldate = {2009-10-01},
booktitle = {Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009},
pages = {36--40},
address = {Corfu, Greece},
abstract = {The increasing availability of on-line music has motivated a growing interest for organizing, commercializing, and delivering this kind of multimedia content. For it, the use of metadata is of utmost importance. Metadata permit organization, indexing, and retrieval of music contents. They are, therefore, a subject of research both from the design and automatic extraction approaches. The present work focuses on this second issue, providing an open source tool for metadata extraction from standard MIDI files. The tool is presented, the utilized metadata are explained, and some applications and experiments are described as examples of its capabilities.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing availability of on-line music has motivated a growing interest for organizing, commercializing, and delivering this kind of multimedia content. For it, the use of metadata is of utmost importance. Metadata permit organization, indexing, and retrieval of music contents. They are, therefore, a subject of research both from the design and automatic extraction approaches. The present work focuses on this second issue, providing an open source tool for metadata extraction from standard MIDI files. The tool is presented, the utilized metadata are explained, and some applications and experiments are described as examples of its capabilities. Rizo, D.; Lemström, K.; Iñesta, J. M.
Ensemble of state-of-the-art methods for polyphonic music comparison Proceedings Article
In: Rauber, A.; Orio, N.; Rizo, D. (Ed.): Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009, pp. 46–51, Corfu, Greece, 2009, ISBN: 978-84-692-6082-1.
Abstract | Links | BibTeX | Tags: PROSEMUS
@inproceedings{k233,
title = {Ensemble of state-of-the-art methods for polyphonic music comparison},
author = {D. Rizo and K. Lemström and J. M. Iñesta},
editor = {A. Rauber and N. Orio and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/233/wemis2009.pdf},
isbn = {978-84-692-6082-1},
year = {2009},
date = {2009-10-01},
urldate = {2009-10-01},
booktitle = {Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009},
pages = {46--51},
address = {Corfu, Greece},
abstract = {Content-based music comparison is a task where no musical similarity measure can perform well in all possible cases. In this paper we will show that a careful combination of different similarity measures in an ensemble measure, will behave more robust than any of the included individual measures when applied as stand-alone measures. For the experiments we have used five state-of-the-art polyphonic similarity measures and three different corpora of polyphonic music.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Content-based music comparison is a task where no musical similarity measure can perform well in all possible cases. In this paper we will show that a careful combination of different similarity measures in an ensemble measure, will behave more robust than any of the included individual measures when applied as stand-alone measures. For the experiments we have used five state-of-the-art polyphonic similarity measures and three different corpora of polyphonic music. Pérez-Sancho, C.
Stochastic Language Models for Music Information Retrieval PhD Thesis
2009.
Abstract | Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@phdthesis{k240,
title = {Stochastic Language Models for Music Information Retrieval},
author = {C. Pérez-Sancho},
editor = {Jorge Calera Rubio José M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/240/phdthesis_cperez.pdf},
year = {2009},
date = {2009-07-01},
address = {Alicante, Spain},
organization = {Universidad de Alicante},
abstract = {Music Information Retrieval (MIR) is an interdisciplinary research area that aims at providing solutions to most problems related to the access to multimedia databases, in particular those with musical content, either in symbolic (MIDI) or audio format. An especially relevant problem is the automatic organization and indexation of data, since carrying out these tasks by hand would require an overwhelming effort for most people and institutions.
One of the most relevant features that can be obtained from a song in order to perform its automatic organization is the musical style, since it is one of the most popular fields used by people when accessing musical databases and catalogs. In this thesis it has been studied to what extent the musical style of a piece can be determined using just the information contained in its score, by applying pattern recognition techniques on melodic and harmonic sequences obtained from musical scores.},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {phdthesis}
}
Music Information Retrieval (MIR) is an interdisciplinary research area that aims at providing solutions to most problems related to the access to multimedia databases, in particular those with musical content, either in symbolic (MIDI) or audio format. An especially relevant problem is the automatic organization and indexation of data, since carrying out these tasks by hand would require an overwhelming effort for most people and institutions.
One of the most relevant features that can be obtained from a song in order to perform its automatic organization is the musical style, since it is one of the most popular fields used by people when accessing musical databases and catalogs. In this thesis it has been studied to what extent the musical style of a piece can be determined using just the information contained in its score, by applying pattern recognition techniques on melodic and harmonic sequences obtained from musical scores. Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.
Genre classification using chords and stochastic language models Journal Article
In: Connection Science, vol. 21, no. 2, pp. 145-159, 2009, ISSN: 0954-0091.
Abstract | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@article{k227,
title = {Genre classification using chords and stochastic language models},
author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta},
issn = {0954-0091},
year = {2009},
date = {2009-05-01},
urldate = {2009-05-01},
journal = {Connection Science},
volume = {21},
number = {2},
pages = {145-159},
abstract = {Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different levels of the genre hierarchy for the techniques employed are presented and discussed.},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different levels of the genre hierarchy for the techniques employed are presented and discussed. Oncina, J.
Optimum Algorithm to Minimize Human Interactions in Sequential Computer Assisted Pattern Recognition Journal Article
In: Pattern Recognition Letters, vol. 30, no. 6, pp. 558-563, 2009, ISSN: 0167-8655.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k226,
title = {Optimum Algorithm to Minimize Human Interactions in Sequential Computer Assisted Pattern Recognition},
author = {J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/226/paper.pdf},
issn = {0167-8655},
year = {2009},
date = {2009-02-01},
journal = {Pattern Recognition Letters},
volume = {30},
number = {6},
pages = {558-563},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Gallego-Sánchez, J.; Calera-Rubio, J.
Improving edge detection in highly noised sheet-metal images Journal Article
In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 43-48, 2009, ISSN: 1550-5790.
Abstract | Links | BibTeX | Tags: ARFAI
@article{k239,
title = {Improving edge detection in highly noised sheet-metal images},
author = {J. Gallego-Sánchez and J. Calera-Rubio},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/239/010.pdf},
issn = {1550-5790},
year = {2009},
date = {2009-01-01},
journal = {IEEE Workshop on Applications of Computer Vision (WACV)},
pages = {43-48},
abstract = {This article proposes a new method for robust and accurate detection of the orientation and the location of an object on low-contrast surfaces in an industrial context. To be more efficient and effective, our method employs only artificial vision. Therefore, productivity is increased since it avoids the use of additional mechanical devices to ensure the accuracy of the system.
The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision.},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {article}
}
This article proposes a new method for robust and accurate detection of the orientation and the location of an object on low-contrast surfaces in an industrial context. To be more efficient and effective, our method employs only artificial vision. Therefore, productivity is increased since it avoids the use of additional mechanical devices to ensure the accuracy of the system.
The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision. Pertusa, A.; Iñesta, J. M.
Note Onset Detection Using One Semitone Filter-Bank For MIREX 2009 Proceedings Article
In: MIREX 2009 - Music Information Retrieval Evaluation eXchange, MIREX Audio Onset Detection, Kobe, Japan., 2009.
Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@inproceedings{k238,
title = {Note Onset Detection Using One Semitone Filter-Bank For MIREX 2009},
author = {A. Pertusa and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/238/PI.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {MIREX 2009 - Music Information Retrieval Evaluation eXchange, MIREX Audio Onset Detection},
address = {Kobe, Japan.},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Calera-Rubio, J.; Bernabeu, J. F.
A probabilistic approach to melodic similarity Proceedings Article
In: Proceedings of MML 2009, pp. 48-53, 2009.
Abstract | Links | BibTeX | Tags: ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA
@inproceedings{k231,
title = {A probabilistic approach to melodic similarity},
author = {J. Calera-Rubio and J. F. Bernabeu},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/231/mml2009Bernabeu.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {Proceedings of MML 2009},
pages = {48-53},
abstract = {Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.},
keywords = {ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance. Micó, L.; Oncina, J.
Experimental Analysis of Insertion Costs in a Naïve Dynamic MDF-Tree Journal Article
In: Lecture Notes in Computer Science, vol. 5524, pp. 402-408, 2009, ISBN: 978-3-642-02171-8.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k230,
title = {Experimental Analysis of Insertion Costs in a Naïve Dynamic MDF-Tree},
author = {L. Micó and J. Oncina},
editor = {Armando J. Pinho Ana Maria Mendonça Helder Araújo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/230/ibpria09.pdf},
isbn = {978-3-642-02171-8},
year = {2009},
date = {2009-01-01},
booktitle = {Pattern Recognition and Image Analysis},
journal = {Lecture Notes in Computer Science},
volume = {5524},
pages = {402-408},
publisher = {LNCS 5524},
address = {Povoa do Varzim},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
2012
Orio, N.; Rauber, A.; Rizo, D.
Introduction to the focused issue on music digital libraries Journal Article
In: International Journal on Digital Libraries, vol. 12, no. 2-3, pp. 51-52, 2012, ISSN: ISSN: 1432-5012.
@article{k294,
title = {Introduction to the focused issue on music digital libraries},
author = {N. Orio and A. Rauber and D. Rizo},
issn = {ISSN: 1432-5012},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
journal = {International Journal on Digital Libraries},
volume = {12},
number = {2-3},
pages = {51-52},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {article}
}
Abreu, J.
Detección de regularidades en contornos 2D, cálculo aproximado de medianas y su aplicación en tareas de clasificación PhD Thesis
2012.
Abstract | BibTeX | Tags: ISIC 2010, TIASA
@phdthesis{k293,
title = {Detección de regularidades en contornos 2D, cálculo aproximado de medianas y su aplicación en tareas de clasificación},
author = {J. Abreu},
editor = {J. R. Rico},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
organization = {Universidad de Alicante},
abstract = {In this work, we address two main problems: identifying the regularities and the construction of average contours from contours encoded by Freeman chain codes. Solutions for those problems are proposed which relies in the information gathered from the Levenshtein edit distance computation. We describe a new method for quantifying the regularity of contours and comparing them, when encoded by Freeman chain codes, in terms of a similarity criterion. The criterion used allows subsequences to be found from the minimal cost edit sequence that specifi and es an alignment of contour segments which are similar. Two external parameters adjust the similarity criterion. The information about each similar part is encoded by strings that represent an average contour region. An explanation of how to construct a prototype based on the identifi and ed regularities is also reviewed. The reliability of the prototypes is eva- luated by replacing contour groups, samples, by new prototypes used as the training set in a classifi and cation task. This way, the size of the data set can be reduced without sensibly affecting its representational power for classifi and cation purposes. Experimental results show that this scheme achieves a reduction in the size of the training data set of about 80% while the classifi and cation error only increases by 0.45% in one of the three data sets studied. Also this thesis presents a new fast algorithm for computing an approximation to the mean between two strings of characters representing a 2D shape and its application to a new Wilson-based editing proce dure. The approximate mean is built by including some symbols from the two original strings. Besides, a greedy approach to this algorithm is studied which allows to reduce the time required to computed an approximate mean. The new dataset editing scheme relaxes the criterion for deleting instances proposed by the Wilson editing procedure. In practice, not all instances misclassifi and ed by their near neighbors are pruned. Instead, an artifi and cial instance is added to the dataset in the hope of successfully classifying the instance in the future. The new artifi and cial instance is the approximated mean of the misclassifi and ed sample and its same-class nearest neighbor. Experiments carried over three widely known databases of contours show the proposed algorithms performs very well in computing the mean of two strings, outperforming methods proposed by other authors. Particularly the low computational time required by the heuristic approach make it very suitable when dealing with long length strings. Results also shows the propo- sed preprocessing scheme can reduce the classifi and cation error in about 83% of trials. There is empirical evidence that using the greedy approximation to compute the approximated mean does not affect the editing procedure performance. Finally, a new algorithm with which to compute an approximation to the mean of a set of strings is presented. The approximated mean is computed through the successive improvements of a partial solution. In each iteration, the edit distance from the partial solution to all the strings in the set are computed, thus accounting for the frequency of each of the edit operations in every position of the approximated mean. A goodness index for edit operations is later computed by multiplying their frequency by the cost. Each operation is tested, starting from that with the highest index, in order to verify whether applying it to the partial solution leads to an improvement. If successful, a new iteration begins from the new approximated mean. The algorithm fi and nishes after all the operations have been examined without a better solution being found. Comparative experiments involving Freeman chain codes encoding 2D shapes show that the quality of the approximated mean string is similar to other approaches but achieves a much faster convergence.},
keywords = {ISIC 2010, TIASA},
pubstate = {published},
tppubtype = {phdthesis}
}
In this work, we address two main problems: identifying the regularities and the construction of average contours from contours encoded by Freeman chain codes. Solutions for those problems are proposed which relies in the information gathered from the Levenshtein edit distance computation. We describe a new method for quantifying the regularity of contours and comparing them, when encoded by Freeman chain codes, in terms of a similarity criterion. The criterion used allows subsequences to be found from the minimal cost edit sequence that specifi and es an alignment of contour segments which are similar. Two external parameters adjust the similarity criterion. The information about each similar part is encoded by strings that represent an average contour region. An explanation of how to construct a prototype based on the identifi and ed regularities is also reviewed. The reliability of the prototypes is eva- luated by replacing contour groups, samples, by new prototypes used as the training set in a classifi and cation task. This way, the size of the data set can be reduced without sensibly affecting its representational power for classifi and cation purposes. Experimental results show that this scheme achieves a reduction in the size of the training data set of about 80% while the classifi and cation error only increases by 0.45% in one of the three data sets studied. Also this thesis presents a new fast algorithm for computing an approximation to the mean between two strings of characters representing a 2D shape and its application to a new Wilson-based editing proce dure. The approximate mean is built by including some symbols from the two original strings. Besides, a greedy approach to this algorithm is studied which allows to reduce the time required to computed an approximate mean. The new dataset editing scheme relaxes the criterion for deleting instances proposed by the Wilson editing procedure. In practice, not all instances misclassifi and ed by their near neighbors are pruned. Instead, an artifi and cial instance is added to the dataset in the hope of successfully classifying the instance in the future. The new artifi and cial instance is the approximated mean of the misclassifi and ed sample and its same-class nearest neighbor. Experiments carried over three widely known databases of contours show the proposed algorithms performs very well in computing the mean of two strings, outperforming methods proposed by other authors. Particularly the low computational time required by the heuristic approach make it very suitable when dealing with long length strings. Results also shows the propo- sed preprocessing scheme can reduce the classifi and cation error in about 83% of trials. There is empirical evidence that using the greedy approximation to compute the approximated mean does not affect the editing procedure performance. Finally, a new algorithm with which to compute an approximation to the mean of a set of strings is presented. The approximated mean is computed through the successive improvements of a partial solution. In each iteration, the edit distance from the partial solution to all the strings in the set are computed, thus accounting for the frequency of each of the edit operations in every position of the approximated mean. A goodness index for edit operations is later computed by multiplying their frequency by the cost. Each operation is tested, starting from that with the highest index, in order to verify whether applying it to the partial solution leads to an improvement. If successful, a new iteration begins from the new approximated mean. The algorithm fi and nishes after all the operations have been examined without a better solution being found. Comparative experiments involving Freeman chain codes encoding 2D shapes show that the quality of the approximated mean string is similar to other approaches but achieves a much faster convergence.
Gallego-Sánchez, A. J.; Calera-Rubio, J.; López, D.
Structural Graph Extraction from Images Proceedings Article
In: Omatu, S.; Santana, Juan F. De Paz; González, S. Rodríguez; Molina, J. M.; a. M. Bernardos, (Ed.): Distributed Computing and Artificial Intelligence, pp. 717-724, Springer Berlin / Heidelberg, 2012, ISBN: 978-3-642-28764-0.
@inproceedings{k289,
title = {Structural Graph Extraction from Images},
author = {A. J. Gallego-Sánchez and J. Calera-Rubio and D. López},
editor = {S. Omatu and Juan F. De Paz Santana and S. Rodríguez González and J. M. Molina and a. M. Bernardos},
isbn = {978-3-642-28764-0},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {Distributed Computing and Artificial Intelligence},
pages = {717-724},
publisher = {Springer Berlin / Heidelberg},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Micó, L.; Oncina, J.
A log square average case algorithm to make insertions in fast similarity search Journal Article
In: Pattern Recognition Letters, vol. 33, no. 9, pp. 1060–1065, 2012.
Links | BibTeX | Tags: MIPRCV, TIASA
@article{k287,
title = {A log square average case algorithm to make insertions in fast similarity search},
author = {L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/287/prl.pdf},
year = {2012},
date = {2012-01-01},
journal = {Pattern Recognition Letters},
volume = {33},
number = {9},
pages = {1060–1065},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
Pertusa, A.; Iñesta, J. M.
Efficient methods for joint estimation of multiple fundamental frequencies in music signals Journal Article
In: EURASIP Journal on Advances in Signal Processing, vol. 2012, no. 1, pp. 27, 2012, ISSN: 1687-6180.
Abstract | BibTeX | Tags: DRIMS
@article{k286,
title = {Efficient methods for joint estimation of multiple fundamental frequencies in music signals},
author = {A. Pertusa and J. M. Iñesta},
issn = {1687-6180},
year = {2012},
date = {2012-01-01},
journal = {EURASIP Journal on Advances in Signal Processing},
volume = {2012},
number = {1},
pages = {27},
abstract = {This study presents efficient techniques for multiple fundamental frequency estimation in music signals. The proposed methodology can infer harmonic patterns from a mixture considering interactions with other sources and evaluate them in a joint estimation scheme. For this purpose, a set of fundamental frequency candidates are first selected at each frame, and several hypothetical combinations of them are generated. Combinations are independently evaluated, and the most likely is selected taking into account the intensity and spectral smoothness of its inferred patterns. The method is extended considering adjacent frames in order to smooth the detection in time, and a pitch tracking stage is finally performed to increase the temporal coherence. The proposed algorithms were evaluated in MIREX contests yielding state of the art results with a very low computational burden.},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {article}
}
This study presents efficient techniques for multiple fundamental frequency estimation in music signals. The proposed methodology can infer harmonic patterns from a mixture considering interactions with other sources and evaluate them in a joint estimation scheme. For this purpose, a set of fundamental frequency candidates are first selected at each frame, and several hypothetical combinations of them are generated. Combinations are independently evaluated, and the most likely is selected taking into account the intensity and spectral smoothness of its inferred patterns. The method is extended considering adjacent frames in order to smooth the detection in time, and a pitch tracking stage is finally performed to increase the temporal coherence. The proposed algorithms were evaluated in MIREX contests yielding state of the art results with a very low computational burden.
López-García, G.; Gallego, A. J.; Dalmau-Espert, J. L.; Molina-Carmona, R.; Compan-Rosique, P.
A Grammatical Approach to the Modeling of an Autonomous Robot Journal Article
In: International Journal of Interactive Multimedia and Artificial Intelligence, vol. 1, no. 5, pp. 30-37, 2012, ISSN: 1989-1660.
BibTeX | Tags:
@article{k517,
title = {A Grammatical Approach to the Modeling of an Autonomous Robot},
author = {G. López-García and A. J. Gallego and J. L. Dalmau-Espert and R. Molina-Carmona and P. Compan-Rosique},
issn = {1989-1660},
year = {2012},
date = {2012-01-01},
journal = {International Journal of Interactive Multimedia and Artificial Intelligence},
volume = {1},
number = {5},
pages = {30-37},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
López-García, G.; Gallego, A. J.; Dalmau-Espert, J. L.; Molina-Carmona, R.; Compan-Rosique, P.
Modeling a Mobile Robot Using a Grammatical Model Proceedings Article
In: Distributed Computing and Artificial Intelligence, pp. 445-452, 2012, ISBN: 978-3-642-28765-7.
BibTeX | Tags:
@inproceedings{k516,
title = {Modeling a Mobile Robot Using a Grammatical Model},
author = {G. López-García and A. J. Gallego and J. L. Dalmau-Espert and R. Molina-Carmona and P. Compan-Rosique},
isbn = {978-3-642-28765-7},
year = {2012},
date = {2012-01-01},
booktitle = {Distributed Computing and Artificial Intelligence},
pages = {445-452},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2011
Rizo, D.; Iñesta, J. M.; Lemström, K.
Polyphonic Music Retrieval with Classifier Ensembles Journal Article
In: Journal of New Music Research, vol. 40, no. 4, pp. 313-324, 2011, ISSN: 0929-8215.
@article{k284,
title = {Polyphonic Music Retrieval with Classifier Ensembles},
author = {D. Rizo and J. M. Iñesta and K. Lemström},
issn = {0929-8215},
year = {2011},
date = {2011-12-01},
urldate = {2011-12-01},
journal = {Journal of New Music Research},
volume = {40},
number = {4},
pages = {313-324},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {article}
}
Iñesta, J. M.; Pérez-García, T.
A Multimodal Music Transcription Prototype Proceedings Article
In: Proc. of International Conference on Multimodal Interaction, ICMI 2011, pp. 315–318, ACM, Alicante, Spain, 2011, ISBN: 978-1-4503-0641-6.
Abstract | BibTeX | Tags: DRIMS, MIPRCV
@inproceedings{k274,
title = {A Multimodal Music Transcription Prototype},
author = {J. M. Iñesta and T. Pérez-García},
isbn = {978-1-4503-0641-6},
year = {2011},
date = {2011-11-01},
urldate = {2011-11-01},
booktitle = {Proc. of International Conference on Multimodal Interaction, ICMI 2011},
pages = {315--318},
publisher = {ACM},
address = {Alicante, Spain},
abstract = {Music transcription consists of transforming an audio signal encoding a music performance in a symbolic representation such as a music score. In this paper, a multimodal and interactive prototype to perform music transcription is
presented. The system is oriented to monotimbral transcription, its working domain is music played by a single instrument. This prototype uses three different sources of information to detect notes in a musical audio excerpt. It has been developed to allow a human expert to interact with the system to improve its results. In its current implementation, it offers a limited range of interaction and multimodality. Further development aimed at full interactivity and multimodal interactions is discussed.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {inproceedings}
}
Music transcription consists of transforming an audio signal encoding a music performance in a symbolic representation such as a music score. In this paper, a multimodal and interactive prototype to perform music transcription is
presented. The system is oriented to monotimbral transcription, its working domain is music played by a single instrument. This prototype uses three different sources of information to detect notes in a musical audio excerpt. It has been developed to allow a human expert to interact with the system to improve its results. In its current implementation, it offers a limited range of interaction and multimodality. Further development aimed at full interactivity and multimodal interactions is discussed.
presented. The system is oriented to monotimbral transcription, its working domain is music played by a single instrument. This prototype uses three different sources of information to detect notes in a musical audio excerpt. It has been developed to allow a human expert to interact with the system to improve its results. In its current implementation, it offers a limited range of interaction and multimodality. Further development aimed at full interactivity and multimodal interactions is discussed.
Higuera, C. De La; Oncina, J.
Finding the most probable string and the consensus string: an algorithmic study Proceedings Article
In: In: 12th International Conference on Parsing Technologies (IWPT 2011), pp. 26-36, Dublin, 2011.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k288,
title = {Finding the most probable string and the consensus string: an algorithmic study},
author = {C. De La Higuera and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/288/iwpt2011.pdf},
year = {2011},
date = {2011-10-01},
urldate = {2011-10-01},
booktitle = {In: 12th International Conference on Parsing Technologies (IWPT 2011)},
pages = {26-36},
address = {Dublin},
abstract = {The problem of finding the most probable string for a distribution generated by a weighted finite automaton is related to a number of important questions: computing the distance between two distributions or finding the best translation (the most probable one) given a probabilistic finite state transducer. The problem is undecidable with general weights and is $NP$-hard if the automaton is probabilistic. In this paper we give a pseudo-polynomial algorithm which computes the most probable string in time polynomial in the inverse of the probability of this string itself. We also give a randomised algorithm solving the same problem and discuss the case where the distribution is generated by other types of machines.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
The problem of finding the most probable string for a distribution generated by a weighted finite automaton is related to a number of important questions: computing the distance between two distributions or finding the best translation (the most probable one) given a probabilistic finite state transducer. The problem is undecidable with general weights and is $NP$-hard if the automaton is probabilistic. In this paper we give a pseudo-polynomial algorithm which computes the most probable string in time polynomial in the inverse of the probability of this string itself. We also give a randomised algorithm solving the same problem and discuss the case where the distribution is generated by other types of machines.
Miotto, R.; Rizo, D.; Orio, N.; Lartillot, O.
MusiCLEF: a Benchmark Activity in Multimodal Music Information Retrieval Proceedings Article
In: Proc. of the 12th International Society for Music Information Retrieval Conference (ISMIR), Miami 2011, pp. 603-608, University of Miami, 2011, ISBN: 978-0-615-54865-4.
@inproceedings{k275,
title = {MusiCLEF: a Benchmark Activity in Multimodal Music Information Retrieval},
author = {R. Miotto and D. Rizo and N. Orio and O. Lartillot},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/275/Orio_etal_Ismir_2011.pdf},
isbn = {978-0-615-54865-4},
year = {2011},
date = {2011-10-01},
urldate = {2011-10-01},
booktitle = {Proc. of the 12th International Society for Music Information Retrieval Conference (ISMIR), Miami 2011},
pages = {603-608},
publisher = {University of Miami},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {inproceedings}
}
León, Pedro J. Ponce
A statistical pattern recognition approach to symbolic music classification PhD Thesis
2011.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV
@phdthesis{k271,
title = {A statistical pattern recognition approach to symbolic music classification},
author = {Pedro J. Ponce León},
editor = {José M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/271/PhD_Pedro_J_Ponce_de_Leon_2011.pdf},
year = {2011},
date = {2011-09-01},
address = {Alicante, Spain},
organization = {University of Alicante},
abstract = {[ENGLISH] This is a work in the field of Music Information Retrieval, from symbolic sources (digital music scores or similar). It applies statistical pattern recognition techniques to approach two different, but related, problems: melody part selection in polyphonic works, and automatic music genre classification.
[ESPAÑOL] El trabajo se enmarca en el dominio de Recuperación de Música por Ordenador, a partir de fuentes simbólicas (partituras digitales o similares). En concreto, se plantean soluciones computacionales mediante la aplicación de técnicas estadísticas de reconocimiento de formas a dos problemas: la selección automática de partes melódicas en obras polifónicas y la clasificación automática de géneros musicales. Entre las posibles aplicaciones de estas técnicas está la catalogación, indexación y recuperación automática de obras musicales, basadas en su contenido, de grandes bases de datos que contienen obras en formato simbólico (partituras digitales, archivos MIDI, etc.). Otras aplicaciones, en el ámbito de la musicología computacional, incluyen la caracterización de géneros musicales y melodías mediante el análisis automático del contenido de grandes volúmenes de obras musicales.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {phdthesis}
}
[ENGLISH] This is a work in the field of Music Information Retrieval, from symbolic sources (digital music scores or similar). It applies statistical pattern recognition techniques to approach two different, but related, problems: melody part selection in polyphonic works, and automatic music genre classification.
[ESPAÑOL] El trabajo se enmarca en el dominio de Recuperación de Música por Ordenador, a partir de fuentes simbólicas (partituras digitales o similares). En concreto, se plantean soluciones computacionales mediante la aplicación de técnicas estadísticas de reconocimiento de formas a dos problemas: la selección automática de partes melódicas en obras polifónicas y la clasificación automática de géneros musicales. Entre las posibles aplicaciones de estas técnicas está la catalogación, indexación y recuperación automática de obras musicales, basadas en su contenido, de grandes bases de datos que contienen obras en formato simbólico (partituras digitales, archivos MIDI, etc.). Otras aplicaciones, en el ámbito de la musicología computacional, incluyen la caracterización de géneros musicales y melodías mediante el análisis automático del contenido de grandes volúmenes de obras musicales.
[ESPAÑOL] El trabajo se enmarca en el dominio de Recuperación de Música por Ordenador, a partir de fuentes simbólicas (partituras digitales o similares). En concreto, se plantean soluciones computacionales mediante la aplicación de técnicas estadísticas de reconocimiento de formas a dos problemas: la selección automática de partes melódicas en obras polifónicas y la clasificación automática de géneros musicales. Entre las posibles aplicaciones de estas técnicas está la catalogación, indexación y recuperación automática de obras musicales, basadas en su contenido, de grandes bases de datos que contienen obras en formato simbólico (partituras digitales, archivos MIDI, etc.). Otras aplicaciones, en el ámbito de la musicología computacional, incluyen la caracterización de géneros musicales y melodías mediante el análisis automático del contenido de grandes volúmenes de obras musicales.
Socorro, R.; Micó, L.; Oncina, J.
A fast pivot-based indexing algorithm for metric spaces Journal Article
In: Pattern Recognition Letters, vol. 32, no. 11, pp. 1511-1516, 2011.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@article{k266,
title = {A fast pivot-based indexing algorithm for metric spaces},
author = {R. Socorro and L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/266/piaesa-prl.pdf},
year = {2011},
date = {2011-08-01},
urldate = {2011-08-01},
journal = {Pattern Recognition Letters},
volume = {32},
number = {11},
pages = {1511-1516},
abstract = {This work focus on fast nearest neighbor (NN) search algorithms that can work in any metric space (not just the Euclidean distance) and where the distance computation is very time consuming. One of the most well known methods in this field is the AESA algorithm, used as baseline for performance measurement for over twenty years. The AESA works in two steps that repeats: first it searches a promising candidate to NN and computes its distance (approximation step), next it eliminates all the unsuitable NN candidates in view of the new information acquired in the previous calculation (elimination step).
This work introduces the PiAESA algorithm. This algorithm improves the performance of the AESA algorithm by splitting the approximation criterion: on the first iterations, when there is not enough information to find good NN candidates, it uses a list of pivots (objects in the database) to obtain a cheap approximation of the distance function. Once a good approximation is obtained it switches to the AESA usual behavior. As the pivot list is built in preprocessing time, the run time of PiAESA is almost the same than the AESA one.
In this work, we report experiments comparing with some competing methods. Our empirical results show that this new approach obtains a significant reduction of distance computations with no execution time penalty.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
This work focus on fast nearest neighbor (NN) search algorithms that can work in any metric space (not just the Euclidean distance) and where the distance computation is very time consuming. One of the most well known methods in this field is the AESA algorithm, used as baseline for performance measurement for over twenty years. The AESA works in two steps that repeats: first it searches a promising candidate to NN and computes its distance (approximation step), next it eliminates all the unsuitable NN candidates in view of the new information acquired in the previous calculation (elimination step).
This work introduces the PiAESA algorithm. This algorithm improves the performance of the AESA algorithm by splitting the approximation criterion: on the first iterations, when there is not enough information to find good NN candidates, it uses a list of pivots (objects in the database) to obtain a cheap approximation of the distance function. Once a good approximation is obtained it switches to the AESA usual behavior. As the pivot list is built in preprocessing time, the run time of PiAESA is almost the same than the AESA one.
In this work, we report experiments comparing with some competing methods. Our empirical results show that this new approach obtains a significant reduction of distance computations with no execution time penalty.
This work introduces the PiAESA algorithm. This algorithm improves the performance of the AESA algorithm by splitting the approximation criterion: on the first iterations, when there is not enough information to find good NN candidates, it uses a list of pivots (objects in the database) to obtain a cheap approximation of the distance function. Once a good approximation is obtained it switches to the AESA usual behavior. As the pivot list is built in preprocessing time, the run time of PiAESA is almost the same than the AESA one.
In this work, we report experiments comparing with some competing methods. Our empirical results show that this new approach obtains a significant reduction of distance computations with no execution time penalty.
Bernabeu, J. F.; Calera-Rubio, J.; Iñesta, J. M.; Rizo, D.
Melodic Identification Using Probabilistic Tree Automata Journal Article
In: Journal of New Music Research, vol. 40, no. 2, pp. 93-103, 2011, ISSN: 0929-8215.
Abstract | BibTeX | Tags: DRIMS, MIPRCV, TIASA
@article{k270,
title = {Melodic Identification Using Probabilistic Tree Automata},
author = {J. F. Bernabeu and J. Calera-Rubio and J. M. Iñesta and D. Rizo},
issn = {0929-8215},
year = {2011},
date = {2011-06-01},
urldate = {2011-06-01},
journal = {Journal of New Music Research},
volume = {40},
number = {2},
pages = {93-103},
abstract = {Similarity computation is a difficult issue in music information retrieval tasks, because it tries to emulate the special ability that humans show for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labelling has proven to be effective in melodic similarity computation. One of the main drawbacks of this approach is that the tree comparison algorithms are of a high time complexity. In this paper, stochastic k-testable tree-models are applied for computing the similarity between two melodies as a probability. The results are compared to those achieved by tree edit distances, showing that k-testable tree-models outperform other reference methods in both recognition rate and efficiency. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and with efficiency for scalability issues.},
keywords = {DRIMS, MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
Similarity computation is a difficult issue in music information retrieval tasks, because it tries to emulate the special ability that humans show for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labelling has proven to be effective in melodic similarity computation. One of the main drawbacks of this approach is that the tree comparison algorithms are of a high time complexity. In this paper, stochastic k-testable tree-models are applied for computing the similarity between two melodies as a probability. The results are compared to those achieved by tree edit distances, showing that k-testable tree-models outperform other reference methods in both recognition rate and efficiency. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and with efficiency for scalability issues.
Oncina, J.; Vidal, E.
Interactive Structured Output Prediction: Application to Chromosome Classification Journal Article
In: Pattern Recognition and Image Analysis (LNCS), vol. 6669, pp. 256-264, 2011.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@article{k267,
title = {Interactive Structured Output Prediction: Application to Chromosome Classification},
author = {J. Oncina and E. Vidal},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/267/karyo.pdf},
year = {2011},
date = {2011-06-01},
urldate = {2011-06-01},
journal = {Pattern Recognition and Image Analysis (LNCS)},
volume = {6669},
pages = {256-264},
abstract = {Interactive Pattern Recognition concepts and techniques are applied to problems with structured output and i.e., problems in which the result is not just a simple class label, but a suitable structure of labels. For illustration purposes (a simplification of) the problem of Human Karyotyping is considered. Results show that a) taking into account label dependencies in a karyogram significantly reduces the classical (noninteractive) chromosome label prediction error rate and b) they are further improved when interactive processing is adopted.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
Interactive Pattern Recognition concepts and techniques are applied to problems with structured output and i.e., problems in which the result is not just a simple class label, but a suitable structure of labels. For illustration purposes (a simplification of) the problem of Human Karyotyping is considered. Results show that a) taking into account label dependencies in a karyogram significantly reduces the classical (noninteractive) chromosome label prediction error rate and b) they are further improved when interactive processing is adopted.
Iñesta, J. M.; Pérez-Sancho, C.; Hontanilla, M.
Composer Recognition using Language Models Proceedings Article
In: Proc. of Signal Processing, Pattern Recognition, and Applications (SPPRA 2011), pp. 76-83, ACTA Press, Innsbruck, Austria, 2011, ISBN: 978-0-88986-865-6.
Abstract | BibTeX | Tags: DRIMS, UA-CPS
@inproceedings{k261,
title = {Composer Recognition using Language Models},
author = {J. M. Iñesta and C. Pérez-Sancho and M. Hontanilla},
isbn = {978-0-88986-865-6},
year = {2011},
date = {2011-02-01},
urldate = {2011-02-01},
booktitle = {Proc. of Signal Processing, Pattern Recognition, and Applications (SPPRA 2011)},
pages = {76-83},
publisher = {ACTA Press},
address = {Innsbruck, Austria},
abstract = {In this paper we present an application of language modeling
techniques using n-grams to an authorship attribution
task. An stylometric study has been conducted on a pair
of datasets of baroque and classical composers, with which
other authors performed previously a similar study using a
set of musicological features and pattern recognition techniques.
In this paper, a simple general-purpose encoding
method has been used, in conjunction with language modeling
to explore the same problem. The results show that
this simpler method can lead to the same conclusions than
other more sophisticated methods, even traditional musicological
studies, without the need of advanced musicological
knowledge for processing the scores.},
keywords = {DRIMS, UA-CPS},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we present an application of language modeling
techniques using n-grams to an authorship attribution
task. An stylometric study has been conducted on a pair
of datasets of baroque and classical composers, with which
other authors performed previously a similar study using a
set of musicological features and pattern recognition techniques.
In this paper, a simple general-purpose encoding
method has been used, in conjunction with language modeling
to explore the same problem. The results show that
this simpler method can lead to the same conclusions than
other more sophisticated methods, even traditional musicological
studies, without the need of advanced musicological
knowledge for processing the scores.
techniques using n-grams to an authorship attribution
task. An stylometric study has been conducted on a pair
of datasets of baroque and classical composers, with which
other authors performed previously a similar study using a
set of musicological features and pattern recognition techniques.
In this paper, a simple general-purpose encoding
method has been used, in conjunction with language modeling
to explore the same problem. The results show that
this simpler method can lead to the same conclusions than
other more sophisticated methods, even traditional musicological
studies, without the need of advanced musicological
knowledge for processing the scores.
Socorro, R.; Micó, L.; Oncina, J.
Efficient search supporting several similarity queries by reordering pivots Proceedings Article
In: Signal Processing, Pattern Recognition, and Applications (SPPRA 2011), pp. 114-120, ACTA Press, Innsbruck, Austria, 2011, ISBN: 978-0-88986-865-6.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k260,
title = {Efficient search supporting several similarity queries by reordering pivots},
author = {R. Socorro and L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/260/sppra.pdf},
isbn = {978-0-88986-865-6},
year = {2011},
date = {2011-02-01},
booktitle = {Signal Processing, Pattern Recognition, and Applications (SPPRA 2011)},
pages = {114-120},
publisher = {ACTA Press},
address = {Innsbruck, Austria},
abstract = {Effective similarity search indexing in general metric spaces has traditionally received special attention in several areas of interest like pattern recognition, computer vision or information retrieval. A typical method is based on the use of a distance as a dissimilarity function (not restricting to Euclidean distance) where the main objective is to speed up the search of the most similar object in a database by
minimising the number of distance computations. Several types of search can be defined, being the k-nearest neighbour or the range search the most common. AESA is one of the most well known of such algorithms due to its performance (measured in distance computations). PiAESA is an AESA variant where the main objective has changed. Instead of trying to find the best nearest neighbour candidate at each step, it tries to find the object that contributes the most to have a bigger lower bound function, that is, a better estimation of the distance. In this paper we extend and test PiAESA to support several similarity queries. Our empirical results show that this approach obtains a significant improvement in performance when comparing with competing algorithms.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Effective similarity search indexing in general metric spaces has traditionally received special attention in several areas of interest like pattern recognition, computer vision or information retrieval. A typical method is based on the use of a distance as a dissimilarity function (not restricting to Euclidean distance) where the main objective is to speed up the search of the most similar object in a database by
minimising the number of distance computations. Several types of search can be defined, being the k-nearest neighbour or the range search the most common. AESA is one of the most well known of such algorithms due to its performance (measured in distance computations). PiAESA is an AESA variant where the main objective has changed. Instead of trying to find the best nearest neighbour candidate at each step, it tries to find the object that contributes the most to have a bigger lower bound function, that is, a better estimation of the distance. In this paper we extend and test PiAESA to support several similarity queries. Our empirical results show that this approach obtains a significant improvement in performance when comparing with competing algorithms.
minimising the number of distance computations. Several types of search can be defined, being the k-nearest neighbour or the range search the most common. AESA is one of the most well known of such algorithms due to its performance (measured in distance computations). PiAESA is an AESA variant where the main objective has changed. Instead of trying to find the best nearest neighbour candidate at each step, it tries to find the object that contributes the most to have a bigger lower bound function, that is, a better estimation of the distance. In this paper we extend and test PiAESA to support several similarity queries. Our empirical results show that this approach obtains a significant improvement in performance when comparing with competing algorithms.
Oncina, J.; Rodríguez, R.
Interactive Text Generation Book Chapter
In: Toselli, A.; Vidal, E.; Casacuberta, F. (Ed.): Multimodal Interactive Pattern Recognition and Applications, Chapter 10, pp. 195-207, Springer, 2011, ISBN: 978-0-85729-478-4.
BibTeX | Tags: MIPRCV, PASCAL2
@inbook{k291,
title = {Interactive Text Generation},
author = {J. Oncina and R. Rodríguez},
editor = {A. Toselli and E. Vidal and F. Casacuberta},
isbn = {978-0-85729-478-4},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {Multimodal Interactive Pattern Recognition and Applications},
pages = {195-207},
publisher = {Springer},
chapter = {10},
keywords = {MIPRCV, PASCAL2},
pubstate = {published},
tppubtype = {inbook}
}
Iñesta, J. M.; Rizo, D.; Illescas, P. R.
Learning melodic analysis rules Technical Report
2011.
Abstract | Links | BibTeX | Tags: DRIMS
@techreport{k276,
title = {Learning melodic analysis rules},
author = {J. M. Iñesta and D. Rizo and P. R. Illescas},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/276/mml2011-melan-final.pdf},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
booktitle = {4th Int.Workshop on Music and Machine Learning},
organization = {NIPS},
abstract = {Automatic musical analysis has been approached from different perspectives: grammars, expert systems, probabilistic models, and model matching have been proposed for implementing tonal analysis. In this work we focus on automatic melodic analysis. One question that arises when building a melodic analysis system using a-priori music theory is whether it is possible to automatically extract analysis rules from examples, and how similar are those learnt rules compared to music theory rules. This work investigates this question, i.e. given a dataset of analyzed melodies our objective is to automatically learn analysis rules and to compare them with music theory rules.},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {techreport}
}
Automatic musical analysis has been approached from different perspectives: grammars, expert systems, probabilistic models, and model matching have been proposed for implementing tonal analysis. In this work we focus on automatic melodic analysis. One question that arises when building a melodic analysis system using a-priori music theory is whether it is possible to automatically extract analysis rules from examples, and how similar are those learnt rules compared to music theory rules. This work investigates this question, i.e. given a dataset of analyzed melodies our objective is to automatically learn analysis rules and to compare them with music theory rules.
Serrano, A.; Micó, L.; Oncina, J.
Impact of the Initialization in Tree-Based Fast Similarity Search Techniques Proceedings Article
In: Pelillo, M.; Hancock, E. R. (Ed.): SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition, pp. 163-176, Springer, Venecia, Italia, 2011, ISBN: 978-3-642-24470-4.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k272,
title = {Impact of the Initialization in Tree-Based Fast Similarity Search Techniques},
author = {A. Serrano and L. Micó and J. Oncina},
editor = {M. Pelillo and E. R. Hancock},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/272/simbad11.pdf},
isbn = {978-3-642-24470-4},
year = {2011},
date = {2011-01-01},
booktitle = {SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition},
pages = {163-176},
publisher = {Springer},
address = {Venecia, Italia},
abstract = {Many fast similarity search techniques relies on the use of pivots (specially selected points in the data set). Using these points, specific structures (indexes) are built speeding up the search when queering. Usually, pivot selection techniques are incremental, being the first one randomly chosen.
This article explores several techniques to choose the first pivot in a tree-based fast similarity search technique. We provide experimental results showing that an adequate choice of this pivot leads to significant reductions in distance computations and time complexity.
Moreover, most pivot tree-based indexes emphasizes in building balanced trees.We provide experimentally and theoretical support that very unbalanced trees can be a better choice than balanced ones.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Many fast similarity search techniques relies on the use of pivots (specially selected points in the data set). Using these points, specific structures (indexes) are built speeding up the search when queering. Usually, pivot selection techniques are incremental, being the first one randomly chosen.
This article explores several techniques to choose the first pivot in a tree-based fast similarity search technique. We provide experimental results showing that an adequate choice of this pivot leads to significant reductions in distance computations and time complexity.
Moreover, most pivot tree-based indexes emphasizes in building balanced trees.We provide experimentally and theoretical support that very unbalanced trees can be a better choice than balanced ones.
This article explores several techniques to choose the first pivot in a tree-based fast similarity search technique. We provide experimental results showing that an adequate choice of this pivot leads to significant reductions in distance computations and time complexity.
Moreover, most pivot tree-based indexes emphasizes in building balanced trees.We provide experimentally and theoretical support that very unbalanced trees can be a better choice than balanced ones.
Calvo-Zaragoza, J.; Rizo, D.; Iñesta, J. M.
A distance for partially labeled trees Journal Article
In: Lecture Notes in Computer Science, vol. 6669, pp. 492–499, 2011, ISSN: 0302-9743.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV
@article{k265,
title = {A distance for partially labeled trees},
author = {J. Calvo-Zaragoza and D. Rizo and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/265/ibpria11-calvo.pdf},
issn = {0302-9743},
year = {2011},
date = {2011-01-01},
journal = {Lecture Notes in Computer Science},
volume = {6669},
pages = {492--499},
abstract = {Trees are a powerful data structure for representing data for which hierarchical
relations can be defined. It has been applied in a number of fields like
image analysis, natural language processing, protein structure, or music
retrieval, to name a few. Procedures for comparing trees are very relevant
in many tasks where tree representations are involved. The computation of
these measures is usually time consuming and different authors have
proposed algorithms that are able to compute them in a reasonable time,
by means of approximated versions of the similarity measure. Other methods
require that the trees are fully labeled for the distance to be computed.
The measure utilized in this paper is able to deal with trees labeled
only at the leaves that runs in $O(|T_1|times|T_2|)$ time. Experiments and
comparative results are provided.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Trees are a powerful data structure for representing data for which hierarchical
relations can be defined. It has been applied in a number of fields like
image analysis, natural language processing, protein structure, or music
retrieval, to name a few. Procedures for comparing trees are very relevant
in many tasks where tree representations are involved. The computation of
these measures is usually time consuming and different authors have
proposed algorithms that are able to compute them in a reasonable time,
by means of approximated versions of the similarity measure. Other methods
require that the trees are fully labeled for the distance to be computed.
The measure utilized in this paper is able to deal with trees labeled
only at the leaves that runs in $O(|T_1|times|T_2|)$ time. Experiments and
comparative results are provided.
relations can be defined. It has been applied in a number of fields like
image analysis, natural language processing, protein structure, or music
retrieval, to name a few. Procedures for comparing trees are very relevant
in many tasks where tree representations are involved. The computation of
these measures is usually time consuming and different authors have
proposed algorithms that are able to compute them in a reasonable time,
by means of approximated versions of the similarity measure. Other methods
require that the trees are fully labeled for the distance to be computed.
The measure utilized in this paper is able to deal with trees labeled
only at the leaves that runs in $O(|T_1|times|T_2|)$ time. Experiments and
comparative results are provided.
Bernabeu, J. F.; Calera-Rubio, J.; Iñesta, J. M.
Classifying melodies using tree grammars Journal Article
In: Lecture Notes in Computer Science, vol. 6669, pp. 572–579, 2011, ISSN: 0302-9743.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV, TIASA
@article{k264,
title = {Classifying melodies using tree grammars},
author = {J. F. Bernabeu and J. Calera-Rubio and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/264/ibpria2011-bernabeu.pdf},
issn = {0302-9743},
year = {2011},
date = {2011-01-01},
journal = {Lecture Notes in Computer Science},
volume = {6669},
pages = {572--579},
abstract = {Similarity computation is a difficult issue in music information retrieval, because it tries to emulate the special ability that humans show
for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what
is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labeling has proven to be effective in melodic similarity computation. In this paper we propose a solution when we have melodies represented by trees for the training but the duration information is not available for the input data. For that, we infer a probabilistic context-free grammar using the information in the trees (duration and pitch) and classify new melodies represented by strings using only the pitch. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and efficient for scalability issues.},
keywords = {DRIMS, MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
Similarity computation is a difficult issue in music information retrieval, because it tries to emulate the special ability that humans show
for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what
is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labeling has proven to be effective in melodic similarity computation. In this paper we propose a solution when we have melodies represented by trees for the training but the duration information is not available for the input data. For that, we infer a probabilistic context-free grammar using the information in the trees (duration and pitch) and classify new melodies represented by strings using only the pitch. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and efficient for scalability issues.
for pattern recognition in general, and particularly in the presence of noisy data. A number of works have addressed the problem of what
is the best representation for symbolic music in this context. The tree representation, using rhythm for defining the tree structure and pitch information for leaf and node labeling has proven to be effective in melodic similarity computation. In this paper we propose a solution when we have melodies represented by trees for the training but the duration information is not available for the input data. For that, we infer a probabilistic context-free grammar using the information in the trees (duration and pitch) and classify new melodies represented by strings using only the pitch. The case study in this paper is to identify a snippet query among a set of songs stored in symbolic format. For it, the utilized method must be able to deal with inexact queries and efficient for scalability issues.
Abreu, J.; Rico-Juan, J. R.
Characterization of contour regularities based on the Levenshtein edit distance Journal Article
In: Pattern Recognition Letters, vol. 32, pp. 1421-1427, 2011.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@article{k263,
title = {Characterization of contour regularities based on the Levenshtein edit distance},
author = {J. Abreu and J. R. Rico-Juan},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/263/2009_J_IbPRIA.pdf},
year = {2011},
date = {2011-01-01},
urldate = {2011-01-01},
journal = {Pattern Recognition Letters},
volume = {32},
pages = {1421-1427},
abstract = {This paper describes a new method for quantifying the regularity of contours and comparing them (when encoded by Freeman chain codes) in terms of a similarity criterion which relies on information gathered from Levenshtein edit distance computation. The criterion used allows subsequences to be found from the minimal cost edit sequence that specifies an alignment of contour segments which are similar. Two external parameters adjust the similarity criterion. The information about each similar part is encoded by strings that represent an average contour region. An explanation of how to construct a prototype based on the identified regularities is also reviewed. The reliability of the prototypes is evaluated by replacing contour groups (samples) by new prototypes used as the training set in a classification task. This way, the size of the data set can be reduced without sensibly affecting its representational power for classification purposes. Experimental results show that this scheme achieves a reduction in the size of the training data set of about 80% while the classification error only increases by 0.45% in one of the three data sets studied.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {article}
}
This paper describes a new method for quantifying the regularity of contours and comparing them (when encoded by Freeman chain codes) in terms of a similarity criterion which relies on information gathered from Levenshtein edit distance computation. The criterion used allows subsequences to be found from the minimal cost edit sequence that specifies an alignment of contour segments which are similar. Two external parameters adjust the similarity criterion. The information about each similar part is encoded by strings that represent an average contour region. An explanation of how to construct a prototype based on the identified regularities is also reviewed. The reliability of the prototypes is evaluated by replacing contour groups (samples) by new prototypes used as the training set in a classification task. This way, the size of the data set can be reduced without sensibly affecting its representational power for classification purposes. Experimental results show that this scheme achieves a reduction in the size of the training data set of about 80% while the classification error only increases by 0.45% in one of the three data sets studied.
2010
Rizo, D.
Symbolic music comparison with tree data structures PhD Thesis
2010.
@phdthesis{k258,
title = {Symbolic music comparison with tree data structures},
author = {D. Rizo},
editor = {J. M. Supervisor: Iñesta},
year = {2010},
date = {2010-11-01},
organization = {Universidad de Alicante},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {phdthesis}
}
Ramírez, R.; Conklin, D.; Anagnostopoulou, C.; Iñesta, J. M.
MML 2010: International Workshop on Machine Learning and Music Proceedings Article
In: Proceedings of the international conference on Multimedia, pp. 1733–1734, ACM ACM, Firenze, Italy, 2010, ISBN: 978-1-60558-933-6.
@inproceedings{k259,
title = {MML 2010: International Workshop on Machine Learning and Music},
author = {R. Ramírez and D. Conklin and C. Anagnostopoulou and J. M. Iñesta},
isbn = {978-1-60558-933-6},
year = {2010},
date = {2010-10-01},
urldate = {2010-10-01},
booktitle = {Proceedings of the international conference on Multimedia},
pages = {1733--1734},
publisher = {ACM},
address = {Firenze, Italy},
organization = {ACM},
abstract = {MML 2010, the International Workshop on Machine Learning and Music, continues a series of workshops related to artificial intelligence and machine learning in music. In this short article the Programme Chairs summarize the content of the workshop.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
MML 2010, the International Workshop on Machine Learning and Music, continues a series of workshops related to artificial intelligence and machine learning in music. In this short article the Programme Chairs summarize the content of the workshop.
Pérez-García, Pérez-Sancho T.
Harmonic and Instrumental Information Fusion for Musical Genre Classification Proceedings Article
In: Proc. of. ACM Multimedia Workshop on Music and Machine Learning (MML 2010), pp. 49–52, ACM, Florence (Italy), 2010, ISBN: 978-1-60558-933-6.
Abstract | BibTeX | Tags: DRIMS, MIPRCV
@inproceedings{k256,
title = {Harmonic and Instrumental Information Fusion for Musical Genre Classification},
author = {Pérez-Sancho T. Pérez-García},
isbn = {978-1-60558-933-6},
year = {2010},
date = {2010-10-01},
booktitle = {Proc. of. ACM Multimedia Workshop on Music and Machine Learning (MML 2010)},
pages = {49--52},
publisher = {ACM},
address = {Florence (Italy)},
abstract = {This paper presents a musical genre classification system
based on the combination of two kinds of information of very
different nature: the instrumentation information contained
in a MIDI file (metadata) and the chords that provide the
harmonic structure of the musical score stored in that file
(content). The fusion of these two information sources gives
a single feature vector that represents the file and to which
classification techniques usually utilized for text categorization
tasks are applied. The classification task is performed
under a probabilistic approach that has improved the results
previously obtained for the same data using the instrumental
or the chord information independently.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a musical genre classification system
based on the combination of two kinds of information of very
different nature: the instrumentation information contained
in a MIDI file (metadata) and the chords that provide the
harmonic structure of the musical score stored in that file
(content). The fusion of these two information sources gives
a single feature vector that represents the file and to which
classification techniques usually utilized for text categorization
tasks are applied. The classification task is performed
under a probabilistic approach that has improved the results
previously obtained for the same data using the instrumental
or the chord information independently.
based on the combination of two kinds of information of very
different nature: the instrumentation information contained
in a MIDI file (metadata) and the chords that provide the
harmonic structure of the musical score stored in that file
(content). The fusion of these two information sources gives
a single feature vector that represents the file and to which
classification techniques usually utilized for text categorization
tasks are applied. The classification task is performed
under a probabilistic approach that has improved the results
previously obtained for the same data using the instrumental
or the chord information independently.
Rauber, A.; Mayer, R.
Feature Selection in a Cartesian Ensemble of Feature Subspace Classifiers for Music Categorisation Proceedings Article
In: Proc. of. ACM Multimedia Workshop on Music and Machine Learning (MML 2010), pp. 53–56, ACM, Florence (Italy), 2010, ISBN: 978-1-60558-933-6.
Abstract | BibTeX | Tags: DRIMS, MIPRCV
@inproceedings{k255,
title = {Feature Selection in a Cartesian Ensemble of Feature Subspace Classifiers for Music Categorisation},
author = {A. Rauber and R. Mayer},
isbn = {978-1-60558-933-6},
year = {2010},
date = {2010-10-01},
urldate = {2010-10-01},
booktitle = {Proc. of. ACM Multimedia Workshop on Music and Machine Learning (MML 2010)},
pages = {53--56},
publisher = {ACM},
address = {Florence (Italy)},
abstract = {We evaluate the impact of feature selection on the classification
accuracy and the achieved dimensionality reduction,
which benefits the time needed on training classification
models. Our classification scheme therein is a Cartesian en-
semble classification system, based on the principle of late
fusion and feature subspaces. These feature subspaces describe
different aspects of the same data set. We use it for
the ensemble classification of multiple feature sets from the
audio and symbolic domains. We present an extensive set
of experiments in the context of music genre classification,
based on Music IR benchmark datasets. We show that while
feature selection does not benefit classification accuracy, it
greatly reduces the dimensionality of each feature subspace,
and thus adds to great gains in the time needed to train the
individual classification models that form the ensemble.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {inproceedings}
}
We evaluate the impact of feature selection on the classification
accuracy and the achieved dimensionality reduction,
which benefits the time needed on training classification
models. Our classification scheme therein is a Cartesian en-
semble classification system, based on the principle of late
fusion and feature subspaces. These feature subspaces describe
different aspects of the same data set. We use it for
the ensemble classification of multiple feature sets from the
audio and symbolic domains. We present an extensive set
of experiments in the context of music genre classification,
based on Music IR benchmark datasets. We show that while
feature selection does not benefit classification accuracy, it
greatly reduces the dimensionality of each feature subspace,
and thus adds to great gains in the time needed to train the
individual classification models that form the ensemble.
accuracy and the achieved dimensionality reduction,
which benefits the time needed on training classification
models. Our classification scheme therein is a Cartesian en-
semble classification system, based on the principle of late
fusion and feature subspaces. These feature subspaces describe
different aspects of the same data set. We use it for
the ensemble classification of multiple feature sets from the
audio and symbolic domains. We present an extensive set
of experiments in the context of music genre classification,
based on Music IR benchmark datasets. We show that while
feature selection does not benefit classification accuracy, it
greatly reduces the dimensionality of each feature subspace,
and thus adds to great gains in the time needed to train the
individual classification models that form the ensemble.
Pérez, A.; Ramírez, R.; Iñesta, J. M.
Modeling violin performances using inductive logic programming Journal Article
In: Intelligent Data Analysis, vol. 14, no. 5, pp. 573–585, 2010, ISSN: 1088-467X.
@article{k253,
title = {Modeling violin performances using inductive logic programming},
author = {A. Pérez and R. Ramírez and J. M. Iñesta},
issn = {1088-467X},
year = {2010},
date = {2010-09-01},
urldate = {2010-09-01},
journal = {Intelligent Data Analysis},
volume = {14},
number = {5},
pages = {573--585},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {article}
}
Iñesta, J. M.; Pérez-Sancho, C.; Pérez-García, T.
Fusión de información armónica e instrumental para la clasificación de géneros musicales Proceedings Article
In: Pérez, Juan Carlos (Ed.): Actas del II Workshop de Reconocimiento de Formas y Análisis de Imágenes (AERFAI), pp. 147-153, AERFAI Ibergarceta Publicaciones S.L., Valencia, Spain, 2010, ISBN: 978-84-92812-66-0.
Abstract | BibTeX | Tags: DRIMS, MIPRCV
@inproceedings{k252,
title = {Fusión de información armónica e instrumental para la clasificación de géneros musicales},
author = {J. M. Iñesta and C. Pérez-Sancho and T. Pérez-García},
editor = {Juan Carlos Pérez},
isbn = {978-84-92812-66-0},
year = {2010},
date = {2010-09-01},
urldate = {2010-09-01},
booktitle = {Actas del II Workshop de Reconocimiento de Formas y Análisis de Imágenes (AERFAI)},
pages = {147-153},
publisher = {Ibergarceta Publicaciones S.L.},
address = {Valencia, Spain},
organization = {AERFAI},
abstract = {En este artículo presentamos un sistema de clasificación de género musical basado en la combinación de dos tipos diferentes de información: la información instrumental contenida en un fichero MIDI y los acordes que proporcionan la estructura armónica de la partitura musical almacenada en dicho fichero. La unión de estas informaciones nos proporciona un único vector de caracteríticas sobre el que se aplican técnicas usadas habitualmente en la clasificación de textos. Finalmente esto nos proporciona un clasificador probabilítico que mejora los resultados obtenidos en trabajos previos en los que se usaba de forma independiente la información instrumental y la información armónica de un fichero MIDI.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {inproceedings}
}
En este artículo presentamos un sistema de clasificación de género musical basado en la combinación de dos tipos diferentes de información: la información instrumental contenida en un fichero MIDI y los acordes que proporcionan la estructura armónica de la partitura musical almacenada en dicho fichero. La unión de estas informaciones nos proporciona un único vector de caracteríticas sobre el que se aplican técnicas usadas habitualmente en la clasificación de textos. Finalmente esto nos proporciona un clasificador probabilítico que mejora los resultados obtenidos en trabajos previos en los que se usaba de forma independiente la información instrumental y la información armónica de un fichero MIDI.
Calera-Rubio, J.; Bernabeu, J. F.
Tree language automata for melody recognition Proceedings Article
In: Pérez, Juan Carlos (Ed.): Actas del II Workshop de Reconocimiento de Formas y Análisis de Imágenes (AERFAI), pp. 17-22, AERFAI IBERGARCETA PUBLICACIONES, S.L., Valencia, Spain, 2010, ISBN: 978-84-92812-66-0.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV, TIASA
@inproceedings{k251,
title = {Tree language automata for melody recognition},
author = {J. Calera-Rubio and J. F. Bernabeu},
editor = {Juan Carlos Pérez},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/251/bernabeuCEDI2010Final.pdf},
isbn = {978-84-92812-66-0},
year = {2010},
date = {2010-09-01},
urldate = {2010-09-01},
booktitle = {Actas del II Workshop de Reconocimiento de Formas y Análisis de Imágenes (AERFAI)},
pages = {17-22},
publisher = {IBERGARCETA PUBLICACIONES, S.L.},
address = {Valencia, Spain},
organization = {AERFAI},
abstract = {The representation of symbolic music by
means of trees has shown to be suitable in
melodic similarity computation. In order to
compare trees, different tree edit distances
have been previously used, being their complexity
a main drawback. In this paper, the application of stochastic k-testable treemodels for computing the similarity between two melodies as a probability, compared to the classical edit distance has been addressed. The results show that k-testable tree-models seem to be adequate for the task, since they outperform other reference methods in both recognition rate and efficiency. The case study in this paper is to identify a snippet query among a set of songs. For it, the utilized method must be able to deal with inexact queries and efficiency
for scalability issues.},
keywords = {DRIMS, MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
The representation of symbolic music by
means of trees has shown to be suitable in
melodic similarity computation. In order to
compare trees, different tree edit distances
have been previously used, being their complexity
a main drawback. In this paper, the application of stochastic k-testable treemodels for computing the similarity between two melodies as a probability, compared to the classical edit distance has been addressed. The results show that k-testable tree-models seem to be adequate for the task, since they outperform other reference methods in both recognition rate and efficiency. The case study in this paper is to identify a snippet query among a set of songs. For it, the utilized method must be able to deal with inexact queries and efficiency
for scalability issues.
means of trees has shown to be suitable in
melodic similarity computation. In order to
compare trees, different tree edit distances
have been previously used, being their complexity
a main drawback. In this paper, the application of stochastic k-testable treemodels for computing the similarity between two melodies as a probability, compared to the classical edit distance has been addressed. The results show that k-testable tree-models seem to be adequate for the task, since they outperform other reference methods in both recognition rate and efficiency. The case study in this paper is to identify a snippet query among a set of songs. For it, the utilized method must be able to deal with inexact queries and efficiency
for scalability issues.
Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.; León, P. J. Ponce; Kersten, S.; Ramírez, R.
Genre classification of music by tonal harmony Journal Article
In: Intelligent Data Analysis, vol. 14, no. 5, pp. 533-545, 2010, ISSN: 1088-467X.
Abstract | BibTeX | Tags: Acc. Int. E-A, DRIMS, PROSEMUS
@article{k232,
title = {Genre classification of music by tonal harmony},
author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta and P. J. Ponce León and S. Kersten and R. Ramírez},
issn = {1088-467X},
year = {2010},
date = {2010-09-01},
urldate = {2010-09-01},
journal = {Intelligent Data Analysis},
volume = {14},
number = {5},
pages = {533-545},
abstract = {In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task.},
keywords = {Acc. Int. E-A, DRIMS, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
In this paper we present a genre classification framework for audio music based on a symbolic classification system. Audio signals are transformed into a symbolic representation of harmony using a chord transcription algorithm, based on the computation of harmonic pitch class profiles. Then, language models built from a ground truth of chord progressions for each genre are used to perform classification. We show that chord progressions are a suitable feature to represent musical genre, as they capture the harmonic rules relevant in each musical period or style. Finally, results using both pure symbolic information and chords transcribed from audio-from-MIDI are compared, in order to evaluate the effects of the transcription process in this task.
Micó, L.; Oncina, J.
A Constant Average Time Algorithm to Allow Insertions in the LAESA Fast Nearest Neighbour Search Index Proceedings Article
In: Proc. of the 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey, pp. 23–26, 2010.
Links | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k257,
title = {A Constant Average Time Algorithm to Allow Insertions in the LAESA Fast Nearest Neighbour Search Index},
author = {L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/257/icpr-2010.pdf},
year = {2010},
date = {2010-08-01},
booktitle = {Proc. of the 20th International Conference on Pattern Recognition, ICPR 2010, Istanbul, Turkey},
pages = {23--26},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Gómez-Ballester, E.; Micó, L.; Thollard, F.; Oncina, J.; Moreno-Seco, F.
Combining Elimination Rules in Tree-Based Nearest Neighbor Search Algorithms Proceedings Article
In: Hancok, E. R.; Wilson, R. C.; Ilkay, T. W.; Escolano, F. (Ed.): Structural, Syntactic, and Statistical Pattern Recognition, pp. 80–89, Springer, Cesme, Turkey, 2010, ISBN: 978-3-642-14979-5.
Abstract | Links | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k249,
title = {Combining Elimination Rules in Tree-Based Nearest Neighbor Search Algorithms},
author = {E. Gómez-Ballester and L. Micó and F. Thollard and J. Oncina and F. Moreno-Seco},
editor = {E. R. Hancok and R. C. Wilson and T. W. Ilkay and F. Escolano},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/249/tr-ssspr2010.pdf},
isbn = {978-3-642-14979-5},
year = {2010},
date = {2010-08-01},
booktitle = {Structural, Syntactic, and Statistical Pattern Recognition},
pages = {80--89},
publisher = {Springer},
address = {Cesme, Turkey},
abstract = {A common activity in many pattern recognition tasks, image processing or clustering techniques involves searching a labeled data set looking for the nearest point to a given unlabelled sample. To reduce the computational overhead when the naive exhaustive search is applied, some fast nearest neighbor search (NNS) algorithms have appeared in the last years. Depending on the structure used to store the training set (usually a tree), different strategies to speed up the search have been defined. In this paper, a new algorithm based on the combination of different pruning rules is proposed. An experimental evaluation and comparison of its behavior with respect to other techniques has been performed, using both real and artificial data.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
A common activity in many pattern recognition tasks, image processing or clustering techniques involves searching a labeled data set looking for the nearest point to a given unlabelled sample. To reduce the computational overhead when the naive exhaustive search is applied, some fast nearest neighbor search (NNS) algorithms have appeared in the last years. Depending on the structure used to store the training set (usually a tree), different strategies to speed up the search have been defined. In this paper, a new algorithm based on the combination of different pruning rules is proposed. An experimental evaluation and comparison of its behavior with respect to other techniques has been performed, using both real and artificial data.
Rizo, D.; Iñesta, J. M.
New partially labelled tree similarity measure: a case study Proceedings Article
In: Hancok, E. R.; Wilson, R. C.; Ilkay, T. W.; Escolano, F. (Ed.): Structural, Syntactic, and Statistical Pattern Recognition, pp. 296–305, Springer, Cesme, Turkey, 2010, ISBN: 978-3-642-14979-5.
@inproceedings{k248,
title = {New partially labelled tree similarity measure: a case study},
author = {D. Rizo and J. M. Iñesta},
editor = {E. R. Hancok and R. C. Wilson and T. W. Ilkay and F. Escolano},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/248/ssspr10-cr.pdf},
isbn = {978-3-642-14979-5},
year = {2010},
date = {2010-08-01},
booktitle = {Structural, Syntactic, and Statistical Pattern Recognition},
pages = {296--305},
publisher = {Springer},
address = {Cesme, Turkey},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {inproceedings}
}
Rico-Juan, J. R.; Abreu, J.
A new editing scheme based on a fast two-string median computation applied to OCR Proceedings Article
In: Hancok, E. R.; Wilson, R. C.; Ilkay, T. W.; Escolano, F. (Ed.): Structural, Syntactic, and Statistical Pattern Recognition, pp. 748–756, Springer, Cesme, Izmir, Turkey, 2010, ISBN: 978-3-642-14979-5.
Abstract | BibTeX | Tags: MIPRCV, TIASA
@inproceedings{k247,
title = {A new editing scheme based on a fast two-string median computation applied to OCR},
author = {J. R. Rico-Juan and J. Abreu},
editor = {E. R. Hancok and R. C. Wilson and T. W. Ilkay and F. Escolano},
isbn = {978-3-642-14979-5},
year = {2010},
date = {2010-08-01},
urldate = {2010-08-01},
booktitle = {Structural, Syntactic, and Statistical Pattern Recognition},
pages = {748--756},
publisher = {Springer},
address = {Cesme, Izmir, Turkey},
abstract = {This paper presents a new fast algorithm to compute an approximation to the median between two strings of characters representing a 2D shape and its application to a new classification scheme to decrease its error rate. The median string results from the application of certain edit operations from the minimum cost edit sequence to one of the original strings. The new dataset editing scheme relaxes the criterion to delete instances proposed by the Wilson Editing Proce- dure. In practice, not all instances misclassified by its near neighbors are pruned. Instead, an artificial instance is added to the dataset expecting to successfully classify the instance on the future. The new artificial instance is the median from the misclassified sample and its same-class nearest neighbor. The experiments over two widely used datasets of handwritten characters show this preprocessing scheme can reduce the classification error in about 78% of trials.},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a new fast algorithm to compute an approximation to the median between two strings of characters representing a 2D shape and its application to a new classification scheme to decrease its error rate. The median string results from the application of certain edit operations from the minimum cost edit sequence to one of the original strings. The new dataset editing scheme relaxes the criterion to delete instances proposed by the Wilson Editing Proce- dure. In practice, not all instances misclassified by its near neighbors are pruned. Instead, an artificial instance is added to the dataset expecting to successfully classify the instance on the future. The new artificial instance is the median from the misclassified sample and its same-class nearest neighbor. The experiments over two widely used datasets of handwritten characters show this preprocessing scheme can reduce the classification error in about 78% of trials.
Lidy, T.; Mayer, R.; Rauber, A.; de León, P. J. Ponce; Pertusa, A.; Iñesta, J. M.
A Cartesian Ensemble of Feature Subspace Classifiers for Music Categorization Proceedings Article
In: Downie, J. Stephen; Veltkamp, Remco C. (Ed.): Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010), pp. 279-284, International Society for Music Information Retrieval International Society for Music Information Retrieval, Utrecht, Netherlands, 2010, ISBN: 978-90-393-53813.
Abstract | Links | BibTeX | Tags: DRIMS
@inproceedings{k246,
title = {A Cartesian Ensemble of Feature Subspace Classifiers for Music Categorization},
author = {T. Lidy and R. Mayer and A. Rauber and P. J. Ponce de León and A. Pertusa and J. M. Iñesta},
editor = {J. Stephen Downie and Remco C. Veltkamp},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/246/ismir2010.pdf},
isbn = {978-90-393-53813},
year = {2010},
date = {2010-08-01},
urldate = {2010-08-01},
booktitle = {Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR 2010)},
pages = {279-284},
publisher = {International Society for Music Information Retrieval},
address = {Utrecht, Netherlands},
organization = {International Society for Music Information Retrieval},
abstract = {We present a cartesian ensemble classification system that is based on the principle of late fusion and feature subspaces. These feature subspaces describe different aspects of the same data set. The framework is built on the Weka machine learning toolkit and able to combine arbitrary feature sets and learning schemes. In our scenario, we use it for the ensemble classification of multiple feature sets from the audio and symbolic domains. We present an extensive set of experiments in the context of music genre classification, based on numerous Music IR benchmark datasets, and evaluate a set of combination/voting rules. The results show that the approach is superior to the best choice of a single algorithm on a single feature set. Moreover, it also releases the user from making this choice explicitly.},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {inproceedings}
}
We present a cartesian ensemble classification system that is based on the principle of late fusion and feature subspaces. These feature subspaces describe different aspects of the same data set. The framework is built on the Weka machine learning toolkit and able to combine arbitrary feature sets and learning schemes. In our scenario, we use it for the ensemble classification of multiple feature sets from the audio and symbolic domains. We present an extensive set of experiments in the context of music genre classification, based on numerous Music IR benchmark datasets, and evaluate a set of combination/voting rules. The results show that the approach is superior to the best choice of a single algorithm on a single feature set. Moreover, it also releases the user from making this choice explicitly.
Verdú-Mas, J. L.
Gramáticas probabilisticas para la desambiguación sintáctica PhD Thesis
2010.
@phdthesis{k262,
title = {Gramáticas probabilisticas para la desambiguación sintáctica},
author = {J. L. Verdú-Mas},
editor = {Jorge Calera Rafael Carrasco},
year = {2010},
date = {2010-01-01},
organization = {Univ. Alicante},
keywords = {MIPRCV, TIASA},
pubstate = {published},
tppubtype = {phdthesis}
}
Iñesta, J. M.; Rizo, D.
Trees and combined methods for monophonic music similarity evaluation Proceedings Article
In: MIREX 2010 - Music Information Retrieval Evaluation eXchange, MIREX Symbolic Melodic Similarity contest, Utrecht, The Nederlands, 2010.
@inproceedings{k254,
title = {Trees and combined methods for monophonic music similarity evaluation},
author = {J. M. Iñesta and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/254/trees.pdf},
year = {2010},
date = {2010-01-01},
urldate = {2010-01-01},
booktitle = {MIREX 2010 - Music Information Retrieval Evaluation eXchange, MIREX Symbolic Melodic Similarity contest},
address = {Utrecht, The Nederlands},
keywords = {DRIMS},
pubstate = {published},
tppubtype = {inproceedings}
}
Pertusa, A.
Computationally efficient methods for polyphonic music transcription PhD Thesis
2010.
Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV
@phdthesis{k244,
title = {Computationally efficient methods for polyphonic music transcription},
author = {A. Pertusa},
editor = {José M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/244/pertusaphd.pdf},
year = {2010},
date = {2010-01-01},
organization = {Universidad de Alicante},
abstract = {Automatic music transcription is a music information retrieval (MIR) task which involves many different disciplines, such as audio signal processing, machine learning, computer science, psychoacoustics and music perception, music theory, and music cognition. The goal of automatic music transcription is to extract a human readable and interpretable representation, like a musical score, from an audio signal. To achieve this goal, it is necessary to estimate the pitches, onset times and durations of the notes, the tempo, the meter and the tonality of a musical piece.
The most obvious application of automatic music transcription is to help a musician to write down the music notation of a performance from an audio recording, which is a time consuming task when it is done by hand. Besides this application, automatic music transcription can also be useful for other MIR tasks, like plagiarism detection, artist identification, genre classification, and composition assistance by changing the instrumentation, the arrangement or the loudness before resynthesizing new pieces. In general, music transcription methods can also provide information about the notes to symbolic music algorithms.
This work addresses the automatic music transcription problem using different strategies. Novel efficient methods are proposed for onset detection (detection of the beginnings of musical events) and multiple fundamental frequency estimation (estimation of the pitches in a polyphonic mixture), using supervised learning and signal processing techniques.
The main contributions of this work can be summarized in the following points:
- An analytical and extensive review of the state of the art methods for onset detection and multiple fundamental frequency estimation.
- The development of an efficient approach for onset detection and the construction of a public ground-truth data set for this task.
- Two novel approaches for multiple pitch estimation of a priori known sounds using supervised learning methods. These algorithms were one of the first machine learning methods proposed for this task.
- A simple iterative cancellation approach, mainly intended to transcribe piano music at a low computational cost.
- Heuristic multiple fundamental frequency algorithms based on signal processing to analyze real music without any a priori knowledge. These methods, which are probably the main contribution of this work, experimentally reached the state of the art for this task with a very low
computational burden.},
keywords = {DRIMS, MIPRCV},
pubstate = {published},
tppubtype = {phdthesis}
}
Automatic music transcription is a music information retrieval (MIR) task which involves many different disciplines, such as audio signal processing, machine learning, computer science, psychoacoustics and music perception, music theory, and music cognition. The goal of automatic music transcription is to extract a human readable and interpretable representation, like a musical score, from an audio signal. To achieve this goal, it is necessary to estimate the pitches, onset times and durations of the notes, the tempo, the meter and the tonality of a musical piece.
The most obvious application of automatic music transcription is to help a musician to write down the music notation of a performance from an audio recording, which is a time consuming task when it is done by hand. Besides this application, automatic music transcription can also be useful for other MIR tasks, like plagiarism detection, artist identification, genre classification, and composition assistance by changing the instrumentation, the arrangement or the loudness before resynthesizing new pieces. In general, music transcription methods can also provide information about the notes to symbolic music algorithms.
This work addresses the automatic music transcription problem using different strategies. Novel efficient methods are proposed for onset detection (detection of the beginnings of musical events) and multiple fundamental frequency estimation (estimation of the pitches in a polyphonic mixture), using supervised learning and signal processing techniques.
The main contributions of this work can be summarized in the following points:
- An analytical and extensive review of the state of the art methods for onset detection and multiple fundamental frequency estimation.
- The development of an efficient approach for onset detection and the construction of a public ground-truth data set for this task.
- Two novel approaches for multiple pitch estimation of a priori known sounds using supervised learning methods. These algorithms were one of the first machine learning methods proposed for this task.
- A simple iterative cancellation approach, mainly intended to transcribe piano music at a low computational cost.
- Heuristic multiple fundamental frequency algorithms based on signal processing to analyze real music without any a priori knowledge. These methods, which are probably the main contribution of this work, experimentally reached the state of the art for this task with a very low
computational burden.
The most obvious application of automatic music transcription is to help a musician to write down the music notation of a performance from an audio recording, which is a time consuming task when it is done by hand. Besides this application, automatic music transcription can also be useful for other MIR tasks, like plagiarism detection, artist identification, genre classification, and composition assistance by changing the instrumentation, the arrangement or the loudness before resynthesizing new pieces. In general, music transcription methods can also provide information about the notes to symbolic music algorithms.
This work addresses the automatic music transcription problem using different strategies. Novel efficient methods are proposed for onset detection (detection of the beginnings of musical events) and multiple fundamental frequency estimation (estimation of the pitches in a polyphonic mixture), using supervised learning and signal processing techniques.
The main contributions of this work can be summarized in the following points:
- An analytical and extensive review of the state of the art methods for onset detection and multiple fundamental frequency estimation.
- The development of an efficient approach for onset detection and the construction of a public ground-truth data set for this task.
- Two novel approaches for multiple pitch estimation of a priori known sounds using supervised learning methods. These algorithms were one of the first machine learning methods proposed for this task.
- A simple iterative cancellation approach, mainly intended to transcribe piano music at a low computational cost.
- Heuristic multiple fundamental frequency algorithms based on signal processing to analyze real music without any a priori knowledge. These methods, which are probably the main contribution of this work, experimentally reached the state of the art for this task with a very low
computational burden.
López-García, G.; Molina-Carmona, A. J. R. Gallego
Formal Model to Integrate Multi-Agent Systems and Interactive Graphic Systems Proceedings Article
In: Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART), pp. 264-267, 2010, ISBN: 978-989-674-022-1.
BibTeX | Tags:
@inproceedings{k518,
title = {Formal Model to Integrate Multi-Agent Systems and Interactive Graphic Systems},
author = {G. López-García and A. J. R. Gallego Molina-Carmona},
isbn = {978-989-674-022-1},
year = {2010},
date = {2010-01-01},
booktitle = {Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART)},
pages = {264-267},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2009
Lidy, T.; Grecu, A.; Rauber, A.; Pertusa, A.; de León, P. J. Ponce; Iñesta, J. M.
A Multi-Feature-Set Multi-Classifier Ensemble Approach For Audio Music Classification Proceedings Article
In: Music Information Retrieval Evaluation eXchange (MIREX 2009), International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL) Kobe, Japan, 2009.
@inproceedings{k243,
title = {A Multi-Feature-Set Multi-Classifier Ensemble Approach For Audio Music Classification},
author = {T. Lidy and A. Grecu and A. Rauber and A. Pertusa and P. J. Ponce de León and J. M. Iñesta},
year = {2009},
date = {2009-10-01},
urldate = {2009-10-01},
booktitle = {Music Information Retrieval Evaluation eXchange (MIREX 2009)},
address = {Kobe, Japan},
organization = {International Music Information Retrieval Systems Evaluation Laboratory (IMIRSEL)},
abstract = {The approach of combining a multitude of audio features and also symbolic features (through transcription of audio to MIDI) for music classification proved useful, as shown previously. We extended the system submitted to MIREX 2008 by including temporal audio features, adding another audio analysis algorithm based on finding templates on music, enhancing the polyphonic audio to MIDI transcription system and using an ensemble of classification models specializing on feature subsets, rather than combining all features to feed a single classifier, like in the previous MIREX.
Recent research in music genre classification hints at a glass ceiling being reached using timbral audio features.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The approach of combining a multitude of audio features and also symbolic features (through transcription of audio to MIDI) for music classification proved useful, as shown previously. We extended the system submitted to MIREX 2008 by including temporal audio features, adding another audio analysis algorithm based on finding templates on music, enhancing the polyphonic audio to MIDI transcription system and using an ensemble of classification models specializing on feature subsets, rather than combining all features to feed a single classifier, like in the previous MIREX.
Recent research in music genre classification hints at a glass ceiling being reached using timbral audio features.
Recent research in music genre classification hints at a glass ceiling being reached using timbral audio features.
Iñesta, J. M.; Pérez-García, T.; Rizo, D.
metamidi: a tool for automatic metadata extraction from MIDI files Proceedings Article
In: Rauber, A.; Orio, N.; Rizo, D. (Ed.): Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009, pp. 36–40, Corfu, Greece, 2009, ISBN: 978-84-692-6082-1.
Abstract | Links | BibTeX | Tags: PROSEMUS
@inproceedings{k234,
title = {metamidi: a tool for automatic metadata extraction from MIDI files},
author = {J. M. Iñesta and T. Pérez-García and D. Rizo},
editor = {A. Rauber and N. Orio and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/234/metamidi.pdf},
isbn = {978-84-692-6082-1},
year = {2009},
date = {2009-10-01},
urldate = {2009-10-01},
booktitle = {Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009},
pages = {36--40},
address = {Corfu, Greece},
abstract = {The increasing availability of on-line music has motivated a growing interest for organizing, commercializing, and delivering this kind of multimedia content. For it, the use of metadata is of utmost importance. Metadata permit organization, indexing, and retrieval of music contents. They are, therefore, a subject of research both from the design and automatic extraction approaches. The present work focuses on this second issue, providing an open source tool for metadata extraction from standard MIDI files. The tool is presented, the utilized metadata are explained, and some applications and experiments are described as examples of its capabilities.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing availability of on-line music has motivated a growing interest for organizing, commercializing, and delivering this kind of multimedia content. For it, the use of metadata is of utmost importance. Metadata permit organization, indexing, and retrieval of music contents. They are, therefore, a subject of research both from the design and automatic extraction approaches. The present work focuses on this second issue, providing an open source tool for metadata extraction from standard MIDI files. The tool is presented, the utilized metadata are explained, and some applications and experiments are described as examples of its capabilities.
Rizo, D.; Lemström, K.; Iñesta, J. M.
Ensemble of state-of-the-art methods for polyphonic music comparison Proceedings Article
In: Rauber, A.; Orio, N.; Rizo, D. (Ed.): Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009, pp. 46–51, Corfu, Greece, 2009, ISBN: 978-84-692-6082-1.
Abstract | Links | BibTeX | Tags: PROSEMUS
@inproceedings{k233,
title = {Ensemble of state-of-the-art methods for polyphonic music comparison},
author = {D. Rizo and K. Lemström and J. M. Iñesta},
editor = {A. Rauber and N. Orio and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/233/wemis2009.pdf},
isbn = {978-84-692-6082-1},
year = {2009},
date = {2009-10-01},
urldate = {2009-10-01},
booktitle = {Proceedings of the Workshop on Exploring Musical Information Spaces, ECDL 2009},
pages = {46--51},
address = {Corfu, Greece},
abstract = {Content-based music comparison is a task where no musical similarity measure can perform well in all possible cases. In this paper we will show that a careful combination of different similarity measures in an ensemble measure, will behave more robust than any of the included individual measures when applied as stand-alone measures. For the experiments we have used five state-of-the-art polyphonic similarity measures and three different corpora of polyphonic music.},
keywords = {PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Content-based music comparison is a task where no musical similarity measure can perform well in all possible cases. In this paper we will show that a careful combination of different similarity measures in an ensemble measure, will behave more robust than any of the included individual measures when applied as stand-alone measures. For the experiments we have used five state-of-the-art polyphonic similarity measures and three different corpora of polyphonic music.
Pérez-Sancho, C.
Stochastic Language Models for Music Information Retrieval PhD Thesis
2009.
Abstract | Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@phdthesis{k240,
title = {Stochastic Language Models for Music Information Retrieval},
author = {C. Pérez-Sancho},
editor = {Jorge Calera Rubio José M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/240/phdthesis_cperez.pdf},
year = {2009},
date = {2009-07-01},
address = {Alicante, Spain},
organization = {Universidad de Alicante},
abstract = {Music Information Retrieval (MIR) is an interdisciplinary research area that aims at providing solutions to most problems related to the access to multimedia databases, in particular those with musical content, either in symbolic (MIDI) or audio format. An especially relevant problem is the automatic organization and indexation of data, since carrying out these tasks by hand would require an overwhelming effort for most people and institutions.
One of the most relevant features that can be obtained from a song in order to perform its automatic organization is the musical style, since it is one of the most popular fields used by people when accessing musical databases and catalogs. In this thesis it has been studied to what extent the musical style of a piece can be determined using just the information contained in its score, by applying pattern recognition techniques on melodic and harmonic sequences obtained from musical scores.},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {phdthesis}
}
Music Information Retrieval (MIR) is an interdisciplinary research area that aims at providing solutions to most problems related to the access to multimedia databases, in particular those with musical content, either in symbolic (MIDI) or audio format. An especially relevant problem is the automatic organization and indexation of data, since carrying out these tasks by hand would require an overwhelming effort for most people and institutions.
One of the most relevant features that can be obtained from a song in order to perform its automatic organization is the musical style, since it is one of the most popular fields used by people when accessing musical databases and catalogs. In this thesis it has been studied to what extent the musical style of a piece can be determined using just the information contained in its score, by applying pattern recognition techniques on melodic and harmonic sequences obtained from musical scores.
One of the most relevant features that can be obtained from a song in order to perform its automatic organization is the musical style, since it is one of the most popular fields used by people when accessing musical databases and catalogs. In this thesis it has been studied to what extent the musical style of a piece can be determined using just the information contained in its score, by applying pattern recognition techniques on melodic and harmonic sequences obtained from musical scores.
Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.
Genre classification using chords and stochastic language models Journal Article
In: Connection Science, vol. 21, no. 2, pp. 145-159, 2009, ISSN: 0954-0091.
Abstract | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS
@article{k227,
title = {Genre classification using chords and stochastic language models},
author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta},
issn = {0954-0091},
year = {2009},
date = {2009-05-01},
urldate = {2009-05-01},
journal = {Connection Science},
volume = {21},
number = {2},
pages = {145-159},
abstract = {Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different levels of the genre hierarchy for the techniques employed are presented and discussed.},
keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},
pubstate = {published},
tppubtype = {article}
}
Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naiumlve Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different levels of the genre hierarchy for the techniques employed are presented and discussed.
Oncina, J.
Optimum Algorithm to Minimize Human Interactions in Sequential Computer Assisted Pattern Recognition Journal Article
In: Pattern Recognition Letters, vol. 30, no. 6, pp. 558-563, 2009, ISSN: 0167-8655.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k226,
title = {Optimum Algorithm to Minimize Human Interactions in Sequential Computer Assisted Pattern Recognition},
author = {J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/226/paper.pdf},
issn = {0167-8655},
year = {2009},
date = {2009-02-01},
journal = {Pattern Recognition Letters},
volume = {30},
number = {6},
pages = {558-563},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}
Gallego-Sánchez, J.; Calera-Rubio, J.
Improving edge detection in highly noised sheet-metal images Journal Article
In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 43-48, 2009, ISSN: 1550-5790.
Abstract | Links | BibTeX | Tags: ARFAI
@article{k239,
title = {Improving edge detection in highly noised sheet-metal images},
author = {J. Gallego-Sánchez and J. Calera-Rubio},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/239/010.pdf},
issn = {1550-5790},
year = {2009},
date = {2009-01-01},
journal = {IEEE Workshop on Applications of Computer Vision (WACV)},
pages = {43-48},
abstract = {This article proposes a new method for robust and accurate detection of the orientation and the location of an object on low-contrast surfaces in an industrial context. To be more efficient and effective, our method employs only artificial vision. Therefore, productivity is increased since it avoids the use of additional mechanical devices to ensure the accuracy of the system.
The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision.},
keywords = {ARFAI},
pubstate = {published},
tppubtype = {article}
}
This article proposes a new method for robust and accurate detection of the orientation and the location of an object on low-contrast surfaces in an industrial context. To be more efficient and effective, our method employs only artificial vision. Therefore, productivity is increased since it avoids the use of additional mechanical devices to ensure the accuracy of the system.
The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision.
The technical core is the treatment of straight line contours that occur in close neighbourhood to each other and with similar orientations. It is a particular problem in stacks of objects but can also occur in other applications. New techniques are introduced to ensure the robustness of the system and to tackle the problem of noise, such as an auto-threshold segmentation process, a new type of histogram and a robust regression method used to compute the result with a higher precision.
Pertusa, A.; Iñesta, J. M.
Note Onset Detection Using One Semitone Filter-Bank For MIREX 2009 Proceedings Article
In: MIREX 2009 - Music Information Retrieval Evaluation eXchange, MIREX Audio Onset Detection, Kobe, Japan., 2009.
Links | BibTeX | Tags: Acc. Int. E-A, PROSEMUS
@inproceedings{k238,
title = {Note Onset Detection Using One Semitone Filter-Bank For MIREX 2009},
author = {A. Pertusa and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/238/PI.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {MIREX 2009 - Music Information Retrieval Evaluation eXchange, MIREX Audio Onset Detection},
address = {Kobe, Japan.},
keywords = {Acc. Int. E-A, PROSEMUS},
pubstate = {published},
tppubtype = {inproceedings}
}
Calera-Rubio, J.; Bernabeu, J. F.
A probabilistic approach to melodic similarity Proceedings Article
In: Proceedings of MML 2009, pp. 48-53, 2009.
Abstract | Links | BibTeX | Tags: ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA
@inproceedings{k231,
title = {A probabilistic approach to melodic similarity},
author = {J. Calera-Rubio and J. F. Bernabeu},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/231/mml2009Bernabeu.pdf},
year = {2009},
date = {2009-01-01},
urldate = {2009-01-01},
booktitle = {Proceedings of MML 2009},
pages = {48-53},
abstract = {Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.},
keywords = {ARFAI, DRIMS, MIPRCV, PROSEMUS, TIASA},
pubstate = {published},
tppubtype = {inproceedings}
}
Melodic similarity is an important research topic in music information retrieval.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.
The representation of symbolic music by means of trees has proven to be suitable
in melodic similarity computation, because they are able to code rhythm in their
structure leaving only pitch representations as a degree of freedom for coding.
In order to compare trees, different edit distances have been previously used.
In this paper, stochastic k-testable tree-models, formerly used in other domains
like structured document compression or natural language processing, have been
used for computing a similarity measure between melody trees as a probability
and their performance has been compared to a classical tree edit distance.
Micó, L.; Oncina, J.
Experimental Analysis of Insertion Costs in a Naïve Dynamic MDF-Tree Journal Article
In: Lecture Notes in Computer Science, vol. 5524, pp. 402-408, 2009, ISBN: 978-3-642-02171-8.
Links | BibTeX | Tags: ARFAI, MIPRCV
@article{k230,
title = {Experimental Analysis of Insertion Costs in a Naïve Dynamic MDF-Tree},
author = {L. Micó and J. Oncina},
editor = {Armando J. Pinho Ana Maria Mendonça Helder Araújo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/230/ibpria09.pdf},
isbn = {978-3-642-02171-8},
year = {2009},
date = {2009-01-01},
booktitle = {Pattern Recognition and Image Analysis},
journal = {Lecture Notes in Computer Science},
volume = {5524},
pages = {402-408},
publisher = {LNCS 5524},
address = {Povoa do Varzim},
keywords = {ARFAI, MIPRCV},
pubstate = {published},
tppubtype = {article}
}