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All years 20242023202220212020201920182017201620152014201320122011201020092008200720062005200420032002200120001999199819971996199519941993199219911990198919880000 All types Journal ArticlesBooksConferencesBook ChaptersProceedings ArticlesPhD ThesesPresentationsTechnical Reports All authors Abreu, J. Adriaans, P. Alacid, B. Alashhab, S. Albarrán, P. Alfaro-Contreras, M. Alonso-Jiménez, P. Anagnostopoulou, C. Arista, N. R. Arroyo, V. Asins, E. Ayllon, E. Aznar, I. Balboa, R. M. Barbancho, A. M. Barbancho, I. Barber, F. Barber, X. Baró, A. Barrachina, R. O. Bellet, A. Belmonte, M. A. Benedí, J. Benetos, E. Berlanga, R. Bernabeu, J. F. Bernabeu, M. Bosch, V. Boyer, L. Bresson, J. Buendía, M. Bustos, A. Calera-Rubio, J. Calvo-Zaragoza, J. Cano-Espinosa, C. Canteras, M. Caparrós, M. Cardoso, J. S. Carrasco, R. C. Casacuberta, F. Castellanos, A. Castellanos, F. J. Castellanos, Francisco José Cazorla, M. Cervera, J. M. Cibirían, R. Coltell, O. Compañ-Rosique, P. Conklin, D. Corbí-Bellot, A. M. Costa, A. Cuenca-Asensi, S. Cuevas-Velasquez, H. Dalmau-Espert, J. L. Danger, R. Delgado, T. Desmond, K. Domenech, J. Doménech, S. Domingo, A. Efford, N. Escalona, F. Espí, D. Esplà-Gomis, M. Esposito, Y. Ezquerro, A. Falcone, G. Ferri, F. Fiore, T. M. M. Fisher, R. B. Forcada, M. L. Fornés, A. Fuente, Cde la Fuente, C. Fuente, C. Fujinaga, I. Galant, J. Gallego, A. J. Gallego-Sánchez, A. J. Gallego-Sánchez, J. Garay-Maestre, U. García, P. García-Doménech, R. V. García-García, F. García-Iasci, P. Garrido, A. Garrido-Alenda, A. Garrido-Munoz, C. Gil, P. Gil, R. Gilabert-Zarco, P. Gómez, E. Gómez, J. A. Gómez-Ballester, E. Gomez-Donoso, F. González, G. González, L. A. González-Barrachina, P. Gracia, I. Gracia, J. M. Gras-Martí, A. Grecu, A. Habrard, A. Hazan, A. Hemming, J. Heredia-Agoiz, J. L. Herrero-Vicente, J. Higuera, C. De La Hontanilla, M. Iglesia-Vayá, M. Illescas, P. R. Iñesta, J. M. Izquierdo, E. Jakas, M. M. Jiménez, V. Jiménez, V. M. Jimeno-Morenilla, A. Jr., J. Hajic Kersten, S. Klapuri, A. Lartillot, O. Lemström, K. León, P. J. Ponce De León, P. J. Ponce León, Pedro J. Ponce Lidy, T. Llixiona, J. López, D. López-García, G. López-Gutiérrez, J. C. Lozano, M. Á. Madueño, A. Maestre, E. Manjón, J. V. Marín, R. Marsden, A. Martínez, M. T. Martínez-Álvarez, A. Martinez-Martin, E. Martínez-Sevilla, J. C. Mas-Candela, E. Mateiu, T. N. Mayer, R. Mazón, J. N. Mencarelli, A. Mico, L. Miotto, R. Molina-Carmona, A. J. R. Gallego Molina-Carmona, R. Mollineda, R. A. Montell, J. À. Morales, A. Moreno-Seco, F. Münnich, S. Nieto-Hidalgo, M. Nuñez-Alcover, Alicia Olivares-Rodríguez, C. Oncina, J. Orio, N. Orozco-Beltrán, D. Ortega-Bastida, J. Ortiz, A. Ortiz-Rojas, S. Pacha, A. Pascual, B. Pascual-León, N. Pérez, A. Pérez-Francisco, M. Pérez-García, Pérez-Sancho T. Pérez-García, T. Pérez-Herranz, F. M. Pérez-Ortiz, J. A. Perez-Sancho, C. Pertusa, A Pertusa, A. Piccoli, H. C. B. Pla, F. Pobil, A. P. Del Pomata, P. A. Pons, A. Pugin, L. Quesada, T. Quiles, J. A. Ramirez, R. Ramírez-Sánchez, G. Rauber, A. Recatalá, G. Regalado, Francisco José Castellanos Regimbal, J. Requena, A. Riba, P. Rico, J. R. Rico-Juan, J. R Rico-Juan, J. R. Ríos-Vila, A. Rizo, D. Rocamora, M. T. Rodríguez, R. Rojas, G. Roldán, J. M. Román, M. A. Rosello, A. Rulot, H. M. Saborit-Torres, J. M. Saenz-Gamboa, J. J. Sáez-Pérez, J. Salamon, J. Salas, F. Salinas, J. M. Salvador, C. Sanches, J. M. Sanches, J. Sánchez, J. A. Sánchez, J. S. Sánchez-Ferrer, A. Sánchez-Hernández, A. Sánchez-Martínez, F. Sanchiz, J. M. Sanz, P. J. Sanz, P. P. Sapp, C. S. Sarti, M. A. Scalco, M. Sebban, M. Segarra, E. Serra, X. Serrano, A. Serrano, J. F. Silla, C. N. Jr. Sober-Mira, J. Socorro, R. Sotoca, J. M. Sroubek, Z. Stefan, I. Suárez, A. Tardón, L. J. Thollard, F. Thomae, M. E. Toselli, A. H. Traver, V. J. Tuijl, B. Tylecek, R. Úbeda, J. I. Valero, S. Valero-Mas, J. J. Varo, M. A. Vayá, Saborit M. Iglesia Verdú-Mas, J. L. Vicente, O. Vidal, E. Viejo, D. Vigliensoni, G. Villegas, J. A. Vivas-Tejuelo, L. A. Yepes, J. A. Zaanen, M. Van All users David RizoJosé Manuel IñestaJorge Calvo-ZaragozaPedro José Ponce de LeónFrancisco J. CastellanosMaría AlfaroAntonio Ríos Vila

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2012

Rico-Juan, J. R.; Iñesta, J. M.

New rank methods for reducing the size of the training set using the nearest neighbor rule Journal Article

In: Pattern Recognition Letters, vol. 33, no. 5, pp. 654–660, 2012, ISSN: 0167-8655.

Abstract | Links | BibTeX | Tags: DRIMS, MIPRCV, TIASA

@article{k283,

title = {New rank methods for reducing the size of the training set using the nearest neighbor rule},

author = {J. R. Rico-Juan and J. M. Iñesta},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/283/rankTrainingSet.pdf},

issn = {0167-8655},

year  = {2012},

date = {2012-04-01},

journal = {Pattern Recognition Letters},

volume = {33},

number = {5},

pages = {654--660},

abstract = {(http://dx.doi.org/10.1016/j.patrec.2011.07.019) 

Some new rank methods to select the best prototypes from a training set are proposed in this paper in order to establish its size according to an external parameter, while maintaining the classification accuracy. The traditional methods that filter the training set in a classification task like editing or condensing have some rules that apply to the set in order to remove outliers or keep some prototypes that help in the classification. In our approach, new voting methods are proposed to compute the prototype probability and help to classify correctly a new sample. This probability is the key to sorting the training set out, so a relevance factor from 0 to 1 is used to select the best candidates for each class whose accumulated probabilities are less than that parameter. This approach makes it possible to select the number of prototypes necessary to maintain or even increase the classification accuracy. The results obtained in different high dimensional databases show that these methods maintain the final error rate while reducing the size of the training set.},

keywords = {DRIMS, MIPRCV, TIASA},

pubstate = {published},

tppubtype = {article}

}

Close

(http://dx.doi.org/10.1016/j.patrec.2011.07.019)
Some new rank methods to select the best prototypes from a training set are proposed in this paper in order to establish its size according to an external parameter, while maintaining the classification accuracy. The traditional methods that filter the training set in a classification task like editing or condensing have some rules that apply to the set in order to remove outliers or keep some prototypes that help in the classification. In our approach, new voting methods are proposed to compute the prototype probability and help to classify correctly a new sample. This probability is the key to sorting the training set out, so a relevance factor from 0 to 1 is used to select the best candidates for each class whose accumulated probabilities are less than that parameter. This approach makes it possible to select the number of prototypes necessary to maintain or even increase the classification accuracy. The results obtained in different high dimensional databases show that these methods maintain the final error rate while reducing the size of the training set.

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/283/rankTrainingSet.pdf

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Rico-Juan, J. R.; Iñesta, J. M.

Confidence voting method ensemble applied to off-line signature verification Journal Article

In: Pattern Analysis and Applications, vol. 15, no. 2, pp. 113–120, 2012, ISSN: 1433-7541.

Abstract | Links | BibTeX | Tags: MIPRCV, TIASA

@article{k290,

title = {Confidence voting method ensemble applied to off-line signature verification},

author = {J. R. Rico-Juan and J. M. Iñesta},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/290/rico_juanConfidenceVotingMethodEmsembleOffLineSignatureVerification.pdf},

issn = {1433-7541},

year  = {2012},

date = {2012-04-01},

journal = {Pattern Analysis and Applications},

volume = {15},

number = {2},

pages = {113--120},

abstract = {In this paper, a new approximation to off-line signature verification is proposed based on two-class classifiers using an expert decisions ensemble. Different methods to extract sets of local and a global features from the target sample are detailed. Also a normalisation by confidence voting method is used in order to decrease the final equal error rate (EER). Each set of features is processed by a single expert, and on the other approach proposed, the decisions of the individual classifiers are combined using weighted votes. Experimental results are given using a subcorpus of the large MCYT signature database for random and skilled forgeries. The results show that the weighted combination outperforms the individual classifiers significantly. The best EER obtained were 6.3% in the case of skilled forgeries and 2.3% in the case of random forgeries.},

keywords = {MIPRCV, TIASA},

pubstate = {published},

tppubtype = {article}

}

Close

In this paper, a new approximation to off-line signature verification is proposed based on two-class classifiers using an expert decisions ensemble. Different methods to extract sets of local and a global features from the target sample are detailed. Also a normalisation by confidence voting method is used in order to decrease the final equal error rate (EER). Each set of features is processed by a single expert, and on the other approach proposed, the decisions of the individual classifiers are combined using weighted votes. Experimental results are given using a subcorpus of the large MCYT signature database for random and skilled forgeries. The results show that the weighted combination outperforms the individual classifiers significantly. The best EER obtained were 6.3% in the case of skilled forgeries and 2.3% in the case of random forgeries.

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/290/rico_juanConfidenceVotingMeth[...]

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Serrano, A.; Micó, L.; Oncina, J.

Restructuring Versus non Restructuring Insertions in MDF Indexes Proceedings Article

In: Carmona, J. Salvador Sa&#769 Pedro Latorre; nchez,; Fred, Ana (Ed.): ICPRAM 2012: 1st International Conference on Pattern Recognition Applications and Methods, pp. 474–480, INSTICC SciTePress, Vilamoura, Portugal, 2012, ISBN: 978-989-8425-98-0.

Abstract | Links | BibTeX | Tags: MIPRCV, TIASA

@inproceedings{k282,

title = {Restructuring Versus non Restructuring Insertions in MDF Indexes},

author = {A. Serrano and L. Micó and J. Oncina},

editor = {J. Salvador Sá Pedro Latorre Carmona and nchez and Ana Fred},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/282/ICPRAM12.pdf},

isbn = {978-989-8425-98-0},

year  = {2012},

date = {2012-02-01},

booktitle = {ICPRAM 2012: 1st International Conference on Pattern Recognition Applications and Methods},

pages = {474--480},

publisher = {SciTePress},

address = {Vilamoura, Portugal},

organization = {INSTICC},

abstract = {MDF tree is a data structure (index) that is used to speed up similarity searches in huge databases. To achieve its goal the indexes should exploit some property of the dissimilarity measure. MDF indexes assume that the dissimilarity measure can be viewed as a distance in a metric space. Moreover, in this framework is assumed that the distance is computationally very expensive and then, counting distance computations is a good measure of the time complexity. 

To tackle with a changing world, a problem arises when new points should be inserted in the index. Efficient algorithms should choose between trying to be efficient in search maintaining the “ideal” structure of the index or trying to be efficient when inserting but worsening the search time. 

In this work we propose an insertion algorithm for MDF trees that focus on optimizing insertion times. The worst case time complexity of the algorithm only depends on the depth of the MDF tree. We compare this algorithm with a similar one that focuses on search time performance. We also study the range of applicability of each one.},

keywords = {MIPRCV, TIASA},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

MDF tree is a data structure (index) that is used to speed up similarity searches in huge databases. To achieve its goal the indexes should exploit some property of the dissimilarity measure. MDF indexes assume that the dissimilarity measure can be viewed as a distance in a metric space. Moreover, in this framework is assumed that the distance is computationally very expensive and then, counting distance computations is a good measure of the time complexity.
To tackle with a changing world, a problem arises when new points should be inserted in the index. Efficient algorithms should choose between trying to be efficient in search maintaining the “ideal” structure of the index or trying to be efficient when inserting but worsening the search time.
In this work we propose an insertion algorithm for MDF trees that focus on optimizing insertion times. The worst case time complexity of the algorithm only depends on the depth of the MDF tree. We compare this algorithm with a similar one that focuses on search time performance. We also study the range of applicability of each one.

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/282/ICPRAM12.pdf

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Vicente, O.; Iñesta, J. M.

Bass track selection in MIDI files and multimodal implications to melody Proceedings Article

In: Carmona, J. Salvador Sa&#769 Pedro Latorre; nchez,; Fred, Ana (Ed.): Proceedings of the Int. Conf. on Pattern Recognition Applications and Methods (ICPRAM 2012), pp. 449–458, INSTICC SciTePress, Vilamoura, Portugal, 2012, ISBN: 978-989-8425-98-0.

Abstract | BibTeX | Tags: DRIMS, MIPRCV

@inproceedings{k285,

title = {Bass track selection in MIDI files and multimodal implications to melody},

author = {O. Vicente and J. M. Iñesta},

editor = {J. Salvador Sá Pedro Latorre Carmona and nchez and Ana Fred},

isbn = {978-989-8425-98-0},

year  = {2012},

date = {2012-02-01},

urldate = {2012-02-01},

booktitle = {Proceedings of the Int. Conf. on Pattern Recognition Applications and Methods (ICPRAM 2012)},

pages = {449--458},

publisher = {SciTePress},

address = {Vilamoura, Portugal},

organization = {INSTICC},

abstract = {Standard MIDI files consist of a number of tracks containing information that can be considered as a symbolic representation of music. Usually each track represents an instrument or voice in a music piece. The goal for this work is to identify the track that contains the bass line. This information is very relevant for a number of tasks like rhythm analysis or harmonic segmentation, among others. It is not easy since a bass line can be performed by very different kinds of instruments. We have approached this problem by using statistical features from the symbolic representation of music and a random forest classifier. The first experiment was to classify a track as bass or non-bass. Then we have tried to select the correct bass track in a multi-track MIDI file. Eventually, we have studied the issue of how different sources of information can help in this latter task. In particular, we have analyzed the interactions between bass and melody information. Yielded results were very accurate and melody track identification was significantly improved when using this kind of multimodal help.},

keywords = {DRIMS, MIPRCV},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Standard MIDI files consist of a number of tracks containing information that can be considered as a symbolic representation of music. Usually each track represents an instrument or voice in a music piece. The goal for this work is to identify the track that contains the bass line. This information is very relevant for a number of tasks like rhythm analysis or harmonic segmentation, among others. It is not easy since a bass line can be performed by very different kinds of instruments. We have approached this problem by using statistical features from the symbolic representation of music and a random forest classifier. The first experiment was to classify a track as bass or non-bass. Then we have tried to select the correct bass track in a multi-track MIDI file. Eventually, we have studied the issue of how different sources of information can help in this latter task. In particular, we have analyzed the interactions between bass and melody information. Yielded results were very accurate and melody track identification was significantly improved when using this kind of multimodal help.

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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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/287/prl.pdf

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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.

BibTeX | Tags: MIPRCV, TIASA

@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}

}

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2011

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}

}

Close

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.

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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}

}

Close

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.

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/288/iwpt2011.pdf

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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}

}

Close

[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.

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/271/PhD_Pedro_J_Ponce_de_Leon_201[...]

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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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/266/piaesa-prl.pdf

Close

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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/267/karyo.pdf

Close

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}

}

Close

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.

Close

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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/260/sppra.pdf

Close

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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/263/2009_J_IbPRIA.pdf

Close

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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/264/ibpria2011-bernabeu.pdf

Close

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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/265/ibpria11-calvo.pdf

Close

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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/272/simbad11.pdf

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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}

}

Close

2010

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}

}

Close

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.

Close

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}

}

Close

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.

Close

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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/251/bernabeuCEDI2010Final.pdf

Close

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}

}

Close

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.

Close

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}

}

Close

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.

Close

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}

}

Close

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.

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/249/tr-ssspr2010.pdf

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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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/257/icpr-2010.pdf

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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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/244/pertusaphd.pdf

Close

Verdú-Mas, J. L.

Gramáticas probabilisticas para la desambiguación sintáctica PhD Thesis

2010.

BibTeX | Tags: MIPRCV, TIASA

@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}

}

Close

2009

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}

}

Close

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.

Close

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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/226/paper.pdf

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Abreu, J.; Rico-Juan, J. R.

Contour regularity extraction based on string edit distance Journal Article

In: Lecture Notes in Computer Science, vol. 5524, pp. 160-167, 2009, ISBN: 0302-9743.

Abstract | BibTeX | Tags: ARFAI, MIPRCV

@article{k228,

title = {Contour regularity extraction based on string edit distance},

author = {J. Abreu and J. R. Rico-Juan},

isbn = {0302-9743},

year  = {2009},

date = {2009-01-01},

urldate = {2009-01-01},

booktitle = {Pattern Recognition and Image Analysis. IbPRIA 2009},

journal = {Lecture Notes in Computer Science},

volume = {5524},

pages = {160-167},

publisher = {Springer},

address = {Pòvoa de Varzim, Portugal},

abstract = {In this paper, we present a new method for constructing prototypes representing a set of contours encoded by Freeman Chain Codes.Our method build new prototypes taking into account similar segments shared between contours instances. The similarity criterion was based on the Levenshtein Edit Distance definition. We also outline how to apply our method to reduce a data set without sensibly affect its representational power for classification purposes. Experimental results shows that our scheme can achieve compressions about 50% while classification error increases only by 0.75%.},

keywords = {ARFAI, MIPRCV},

pubstate = {published},

tppubtype = {article}

}

Close

In this paper, we present a new method for constructing prototypes representing a set of contours encoded by Freeman Chain Codes.Our method build new prototypes taking into account similar segments shared between contours instances. The similarity criterion was based on the Levenshtein Edit Distance definition. We also outline how to apply our method to reduce a data set without sensibly affect its representational power for classification purposes. Experimental results shows that our scheme can achieve compressions about 50% while classification error increases only by 0.75%.

Close

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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/230/ibpria09.pdf

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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}

}

Close

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.

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/231/mml2009Bernabeu.pdf

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2008

Gómez-Ballester, E.; Micó, L.; Oncina, J.

A pruning Rule Based on a Distance Sparse Table for Hierarchical Similarity Search Algorithms Journal Article

In: Lecture Notes in Computer Science, vol. 5342, pp. 936-946, 2008.

Links | BibTeX | Tags: ARFAI, MIPRCV

@article{k220,

title = {A pruning Rule Based on a Distance Sparse Table for Hierarchical Similarity Search Algorithms},

author = {E. Gómez-Ballester and L. Micó and J. Oncina},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/220/spr-table.pdf},

year  = {2008},

date = {2008-12-04},

journal = {Lecture Notes in Computer Science},

volume = {5342},

pages = {936-946},

keywords = {ARFAI, MIPRCV},

pubstate = {published},

tppubtype = {article}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/220/spr-table.pdf

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Iñesta, J. M.; Pertusa, A.

Multiple Fundamental Frequency estimation using Gaussian smoothness Proceedings Article

In: Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP 2008, pp. 105-108, Las Vegas, USA, 2008, ISBN: 1-4244-1484-9.

Links | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS

@inproceedings{k202,

title = {Multiple Fundamental Frequency estimation using Gaussian smoothness},

author = {J. M. Iñesta and A. Pertusa},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/202/2974_sent.pdf},

isbn = {1-4244-1484-9},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

booktitle = {Proc. of the IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, ICASSP 2008},

pages = {105-108},

address = {Las Vegas, USA},

keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},

pubstate = {published},

tppubtype = {inproceedings}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/202/2974_sent.pdf

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Pérez-Sancho, C.; Rizo, D.; Kersten, S.; Ramírez, R.

Genre classification of music by tonal harmony Proceedings Article

In: Proc. Int. Workshop on Machine Learning and Music, MML 2008, pp. 21-22, Helsinki, Finland, 2008.

BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS

@inproceedings{k209,

title = {Genre classification of music by tonal harmony},

author = {C. Pérez-Sancho and D. Rizo and S. Kersten and R. Ramírez},

year  = {2008},

date = {2008-01-01},

booktitle = {Proc. Int. Workshop on Machine Learning and Music, MML 2008},

pages = {21-22},

address = {Helsinki, Finland},

keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},

pubstate = {published},

tppubtype = {inproceedings}

}

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Rizo, D.; Illescas, P. R.

Learning to analyse tonal music Proceedings Article

In: Proc. Int. Workshop on Machine Learning and Music, MML 2008, pp. 25-26, Helsinki, Finland, 2008.

BibTeX | Tags: MIPRCV, PROSEMUS

@inproceedings{k210,

title = {Learning to analyse tonal music},

author = {D. Rizo and P. R. Illescas},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

booktitle = {Proc. Int. Workshop on Machine Learning and Music, MML 2008},

pages = {25-26},

address = {Helsinki, Finland},

keywords = {MIPRCV, PROSEMUS},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Pérez-Sancho, C.; Rizo, D.; Iñesta, J. M.

Stochastic text models for music categorization Journal Article

In: Lecture Notes in Computer Science, vol. 5342, pp. 55-64, 2008.

Links | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS

@article{k217,

title = {Stochastic text models for music categorization},

author = {C. Pérez-Sancho and D. Rizo and J. M. Iñesta},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/217/music-cat.pdf},

year  = {2008},

date = {2008-01-01},

journal = {Lecture Notes in Computer Science},

volume = {5342},

pages = {55-64},

keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},

pubstate = {published},

tppubtype = {article}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/217/music-cat.pdf

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Habrard, A.; Iñesta, J. M.; Rizo, D.; Sebban, M.

Melody recognition with learned edit distances Journal Article

In: Lecture Notes in Computer Science, vol. 5342, pp. 86-96, 2008.

Links | BibTeX | Tags: MIPRCV, PROSEMUS

@article{k218,

title = {Melody recognition with learned edit distances},

author = {A. Habrard and J. M. Iñesta and D. Rizo and M. Sebban},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/218/melody-rec.pdf},

year  = {2008},

date = {2008-01-01},

journal = {Lecture Notes in Computer Science},

volume = {5342},

pages = {86-96},

keywords = {MIPRCV, PROSEMUS},

pubstate = {published},

tppubtype = {article}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/218/melody-rec.pdf

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Olivares-Rodríguez, C.; Oncina, J.

A Stochastic Approach to Median String Computation Journal Article

In: Lecture Notes in Computer Science, vol. 5342, pp. 431–440, 2008.

Links | BibTeX | Tags: ARFAI, MIPRCV

@article{k223,

title = {A Stochastic Approach to Median String Computation},

author = {C. Olivares-Rodríguez and J. Oncina},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/223/median.pdf},

year  = {2008},

date = {2008-01-01},

journal = {Lecture Notes in Computer Science},

volume = {5342},

pages = {431–440},

keywords = {ARFAI, MIPRCV},

pubstate = {published},

tppubtype = {article}

}

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Pertusa, A.; Iñesta, J. M.

Multiple Fundamental Frequency Estimation Using Gaussian Smoothness And Short Context. Proceedings Article

In: MIREX 2008 - Music Information Retrieval Evaluation eXchange, MIREX Fundamental Frequency Estimation & Tracking Contest., Philadelphia, Pennsylvania, USA, 2008.

Links | BibTeX | Tags: Acc. Int. E-A, MIPRCV, PROSEMUS

@inproceedings{k237,

title = {Multiple Fundamental Frequency Estimation Using Gaussian Smoothness And Short Context.},

author = {A. Pertusa and J. M. Iñesta},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/237/F0_pertusa.pdf},

year  = {2008},

date = {2008-01-01},

urldate = {2008-01-01},

booktitle = {MIREX 2008 - Music Information Retrieval Evaluation eXchange, MIREX Fundamental Frequency Estimation & Tracking Contest.},

address = {Philadelphia, Pennsylvania, USA},

keywords = {Acc. Int. E-A, MIPRCV, PROSEMUS},

pubstate = {published},

tppubtype = {inproceedings}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/237/F0_pertusa.pdf

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