2018
Pertusa, A.; Gallego, A. J.; Bernabeu, M.
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks Journal Article
In: Neurocomputing, vol. 293, pp. 87-99, 2018, ISSN: 0925-2312.
Abstract | BibTeX | Tags: TIMuL
@article{k366,
title = {MirBot: A collaborative object recognition system for smartphones using convolutional neural networks},
author = {A. Pertusa and A. J. Gallego and M. Bernabeu},
issn = {0925-2312},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Neurocomputing},
volume = {293},
pages = {87-99},
abstract = {MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neural network (CNN). The answers provided by the system can be validated by the user so as to improve the results for future queries. All the images are stored together with a series of metadata, thus enabling a multimodal incremental dataset labeled with synset identifiers from the WordNet ontology. This dataset grows continuously thanks to the users' feedback, and is publicly available for research. This work details the MirBot object recognition system, analyzes the statistics gathered after more than four years of usage, describes the image classification methodology, and performs an exhaustive evaluation using handcrafted features, neural codes, different transfer learning techniques, PCA compression and metadata, which can be used to improve the image classifier results. The app is freely available at the Apple and Google Play stores.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neural network (CNN). The answers provided by the system can be validated by the user so as to improve the results for future queries. All the images are stored together with a series of metadata, thus enabling a multimodal incremental dataset labeled with synset identifiers from the WordNet ontology. This dataset grows continuously thanks to the users' feedback, and is publicly available for research. This work details the MirBot object recognition system, analyzes the statistics gathered after more than four years of usage, describes the image classification methodology, and performs an exhaustive evaluation using handcrafted features, neural codes, different transfer learning techniques, PCA compression and metadata, which can be used to improve the image classifier results. The app is freely available at the Apple and Google Play stores. Gallego, A. J.; Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R.
Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation Journal Article
In: Pattern Recognition, vol. 74, pp. 531-543, 2018.
@article{k378,
title = {Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation},
author = {A. J. Gallego and J. Calvo-Zaragoza and J. J. Valero-Mas and J. R. Rico-Juan},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Pattern Recognition},
volume = {74},
pages = {531-543},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Iñesta, J. M.; Conklin, D.; Ramírez, R.; Fiore, T. M. M.
Machine Learning and Music Generation Book
Routledge, Taylor & Francis, 2018, ISBN: 978-0-8153-7720-7.
@book{k384,
title = {Machine Learning and Music Generation},
author = {J. M. Iñesta and D. Conklin and R. Ramírez and T. M. M. Fiore},
editor = {Thomas M. Fiore},
isbn = {978-0-8153-7720-7},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
pages = {111},
publisher = {Routledge, Taylor & Francis},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {book}
}
2017
Calvo-Zaragoza, J.; Gallego, A. J.; Pertusa, A.
Recognition of Handwritten Music Symbols with Convolutional Neural Codes Proceedings Article
In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 691–696, Kyoto, Japan, 2017.
BibTeX | Tags: GRE16-14, TIMuL
@inproceedings{k376,
title = {Recognition of Handwritten Music Symbols with Convolutional Neural Codes},
author = {J. Calvo-Zaragoza and A. J. Gallego and A. Pertusa},
year = {2017},
date = {2017-11-01},
urldate = {2017-11-01},
booktitle = {14th IAPR International Conference on Document Analysis and Recognition (ICDAR)},
pages = {691--696},
address = {Kyoto, Japan},
keywords = {GRE16-14, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Sober-Mira, J.; Calvo-Zaragoza, J.; Rizo, D.; Iñesta, J. M.
Pen-based music document transcription Proceedings Article
In: Proceedings of GREC 2017, pp. 21–22, IEEE computer society, Kyoto (Japan), 2017, ISBN: 978-1-5386-3586-5.
@inproceedings{k381,
title = {Pen-based music document transcription},
author = {J. Sober-Mira and J. Calvo-Zaragoza and D. Rizo and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/381/3586c021.pdf},
isbn = {978-1-5386-3586-5},
year = {2017},
date = {2017-11-01},
booktitle = {Proceedings of GREC 2017},
pages = {21--22},
publisher = {IEEE computer society},
address = {Kyoto (Japan)},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Calvo-Zaragoza, J.; Valero-Mas, J. J.; Pertusa, A
End-To-End Optical Music Recognition using Neural Networks Proceedings Article
In: Proc. of International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China, 2017.
Abstract | BibTeX | Tags: GRE16-14, TIMuL
@inproceedings{k374,
title = {End-To-End Optical Music Recognition using Neural Networks},
author = {J. Calvo-Zaragoza and J. J. Valero-Mas and A Pertusa},
year = {2017},
date = {2017-10-01},
booktitle = {Proc. of International Society for Music Information Retrieval Conference (ISMIR)},
address = {Suzhou, China},
abstract = {This work addresses the Optical Music Recognition (OMR) task in an end-to-end fashion using neural net- works. The proposed architecture is based on a Recurrent Convolutional Neural Network topology that takes as input an image of a monophonic score and retrieves a sequence of music symbols as output. In the first stage, a series of convolutional filters are trained to extract meaningful fea- tures of the input image, and then a recurrent block models the sequential nature of music. The system is trained us- ing a Connectionist Temporal Classification loss function, which avoids the need for a frame-by-frame alignment be- tween the image and the ground-truth music symbols. Ex- perimentation has been carried on a set of 90,000 synthetic monophonic music scores with more than 50 different pos- sible labels. Results obtained depict classification error rates around 2 % at symbol level, thus proving the po- tential of the proposed end-to-end architecture for OMR. The source code, dataset, and trained models are publicly released for reproducible research and future comparison purposes.},
keywords = {GRE16-14, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
This work addresses the Optical Music Recognition (OMR) task in an end-to-end fashion using neural net- works. The proposed architecture is based on a Recurrent Convolutional Neural Network topology that takes as input an image of a monophonic score and retrieves a sequence of music symbols as output. In the first stage, a series of convolutional filters are trained to extract meaningful fea- tures of the input image, and then a recurrent block models the sequential nature of music. The system is trained us- ing a Connectionist Temporal Classification loss function, which avoids the need for a frame-by-frame alignment be- tween the image and the ground-truth music symbols. Ex- perimentation has been carried on a set of 90,000 synthetic monophonic music scores with more than 50 different pos- sible labels. Results obtained depict classification error rates around 2 % at symbol level, thus proving the po- tential of the proposed end-to-end architecture for OMR. The source code, dataset, and trained models are publicly released for reproducible research and future comparison purposes. Hontanilla, M.; Pérez-Sancho, C.; Iñesta, J. M.
Music style recognition with language models -- beyond statistical results Proceedings Article
In: Proceedings of MML 2017, pp. 31–36, Barcelona, 2017.
@inproceedings{k379,
title = {Music style recognition with language models -- beyond statistical results},
author = {M. Hontanilla and C. Pérez-Sancho and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/379/mml17proceedings-35.pdf},
year = {2017},
date = {2017-10-01},
booktitle = {Proceedings of MML 2017},
pages = {31--36},
address = {Barcelona},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Valero-Mas, J. J.
Towards Interactive Multimodal Music Transcription PhD Thesis
2017.
@phdthesis{k371,
title = {Towards Interactive Multimodal Music Transcription},
author = {J. J. Valero-Mas},
editor = {José M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/371/Thesis.pdf},
year = {2017},
date = {2017-07-01},
urldate = {2017-07-01},
organization = {University of Alicante},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {phdthesis}
}
Bernabeu, J. F.
Similarity Learning and Stochastic Language Models for Tree-Represented Music PhD Thesis
2017.
@phdthesis{k377,
title = {Similarity Learning and Stochastic Language Models for Tree-Represented Music},
author = {J. F. Bernabeu},
editor = {José M. Iñesta and J. Calera-Rubio},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/377/BernabeuPhD.pdf},
year = {2017},
date = {2017-07-01},
organization = {University of Alicante},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {phdthesis}
}
Valero-Mas, J. J.; Benetos, E.; Iñesta, J. M.
Assessing the Relevance of Onset Information for Note Tracking in Piano Music Transcription Proceedings Article
In: Proceedings of the AES International Conference on Semantic Audio, Eerlangen, 2017.
@inproceedings{k363,
title = {Assessing the Relevance of Onset Information for Note Tracking in Piano Music Transcription},
author = {J. J. Valero-Mas and E. Benetos and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/363/CameraReady.pdf},
year = {2017},
date = {2017-06-21},
booktitle = {Proceedings of the AES International Conference on Semantic Audio},
address = {Eerlangen},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Valero-Mas, J. J.; Calvo-Zaragoza, J.; Rico-Juan, J. R.; Iñesta, J. M.
A study of Prototype Selection algorithms for Nearest Neighbour in class-imbalanced problems Proceedings Article
In: Alexandre, J. S. Sánchez L. A.; Rodrigues, J. M. F. (Ed.): Proceedings of the 8th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), pp. 335–343, Springer, Faro, Portugal, 2017, ISBN: 978-3-319-58837-7.
@inproceedings{k362,
title = {A study of Prototype Selection algorithms for Nearest Neighbour in class-imbalanced problems},
author = {J. J. Valero-Mas and J. Calvo-Zaragoza and J. R. Rico-Juan and J. M. Iñesta},
editor = {J. S. Sánchez L. A. Alexandre and J. M. F. Rodrigues},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/362/CameraReady.pdf},
isbn = {978-3-319-58837-7},
year = {2017},
date = {2017-06-01},
booktitle = {Proceedings of the 8th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)},
pages = {335--343},
publisher = {Springer},
address = {Faro, Portugal},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizo, D.; Calvo-Zaragoza, J.; Iñesta, J. M.; Fujinaga, I.
About agnostic representation of musical documents for Optical Music Recognition Proceedings Article
In: Music Encoding Conference, Tours, 2017, 2017.
@inproceedings{k369,
title = {About agnostic representation of musical documents for Optical Music Recognition},
author = {D. Rizo and J. Calvo-Zaragoza and J. M. Iñesta and I. Fujinaga},
year = {2017},
date = {2017-05-01},
booktitle = {Music Encoding Conference, Tours, 2017},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Calvo-Zaragoza, J.; Oncina, J.
Recognition of Pen-based Music Notation with Finite-State Machines Journal Article
In: Expert Systems With Applications, vol. 72, pp. 395-406, 2017.
@article{k358,
title = {Recognition of Pen-based Music Notation with Finite-State Machines},
author = {J. Calvo-Zaragoza and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/358/recognition-pen-based.pdf},
year = {2017},
date = {2017-04-01},
journal = {Expert Systems With Applications},
volume = {72},
pages = {395-406},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Oncina, J.
An efficient approach for Interactive Sequential Pattern Recognition Journal Article
In: Pattern Recognition, vol. 64, pp. 295-304, 2017.
@article{k359,
title = {An efficient approach for Interactive Sequential Pattern Recognition},
author = {J. Calvo-Zaragoza and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/359/efficient-approach-sequential.pdf},
year = {2017},
date = {2017-04-01},
journal = {Pattern Recognition},
volume = {64},
pages = {295-304},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Valero-Mas, J. J.; Calvo-Zaragoza, J.; Rico-Juan, J. R.; Iñesta, J. M.
An Experimental Study on Rank Methods for Prototype Selection Journal Article
In: Soft Computing, vol. 21, no. 19, pp. 5703-–5715, 2017, ISSN: 1432-7643.
Abstract | Links | BibTeX | Tags: TIMuL
@article{k339,
title = {An Experimental Study on Rank Methods for Prototype Selection},
author = {J. J. Valero-Mas and J. Calvo-Zaragoza and J. R. Rico-Juan and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/339/SOCO-RankMethods-2016.pdf},
issn = {1432-7643},
year = {2017},
date = {2017-01-01},
journal = {Soft Computing},
volume = {21},
number = {19},
pages = {5703-–5715},
abstract = {Prototype selection is one of the most popular approaches for addressing the low efficiency issue typically found in the well-known k-Nearest Neighbour classification rule. These techniques select a representative subset from an original collection of prototypes with the premise of main- taining the same classification accuracy. Most recently, rank methods have been proposed as an alternative to develop new selection strategies. Following a certain heuristic, these methods sort the elements of the initial collection accord- ing to their relevance and then select the best possible subset by means of a parameter representing the amount of data to maintain. Due to the relative novelty of these methods, their performance and competitiveness against other strategies is still unclear. This work performs an exhaustive experimental study of such methods for prototype selection. A represen- tative collection of both classic and sophisticated algorithms are compared to the aforementioned techniques in a num- ber of datasets, including different levels of induced noise. Results report the remarkable competitiveness of these rank methods as well as their excellent trade-off between proto- type reduction and achieved accuracy.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Prototype selection is one of the most popular approaches for addressing the low efficiency issue typically found in the well-known k-Nearest Neighbour classification rule. These techniques select a representative subset from an original collection of prototypes with the premise of main- taining the same classification accuracy. Most recently, rank methods have been proposed as an alternative to develop new selection strategies. Following a certain heuristic, these methods sort the elements of the initial collection accord- ing to their relevance and then select the best possible subset by means of a parameter representing the amount of data to maintain. Due to the relative novelty of these methods, their performance and competitiveness against other strategies is still unclear. This work performs an exhaustive experimental study of such methods for prototype selection. A represen- tative collection of both classic and sophisticated algorithms are compared to the aforementioned techniques in a num- ber of datasets, including different levels of induced noise. Results report the remarkable competitiveness of these rank methods as well as their excellent trade-off between proto- type reduction and achieved accuracy. Calvo-Zaragoza, J.; Vigliensoni, G.; Fujinaga, I.
A machine learning framework for the categorization of elements in images of musical documents Proceedings Article
In: Proceedings of the Third International Conference on Technologies for Music Notation and Representation, 2017.
@inproceedings{k360,
title = {A machine learning framework for the categorization of elements in images of musical documents},
author = {J. Calvo-Zaragoza and G. Vigliensoni and I. Fujinaga},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/360/tenor-unified-categorization.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the Third International Conference on Technologies for Music Notation and Representation},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Calvo-Zaragoza, J.; Vigliensoni, G.; Fujinaga, I.
Pixel-wise Binarization of Musical Documents with Convolutional Neural Networks Proceedings Article
In: Proceedings of the 15th IAPR International Conference on Machine Vision Applications, 2017.
@inproceedings{k361,
title = {Pixel-wise Binarization of Musical Documents with Convolutional Neural Networks},
author = {J. Calvo-Zaragoza and G. Vigliensoni and I. Fujinaga},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/361/pixel-wise-binarization.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the 15th IAPR International Conference on Machine Vision Applications},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Calvo-Zaragoza, J.; Pertusa, A.; Oncina, J.
Staff-line detection and removal using a convolutional neural network Journal Article
In: Machine Vision and Applications, pp. 1-10, 2017, ISSN: 1432-1769.
Abstract | BibTeX | Tags: TIMuL
@article{k365,
title = {Staff-line detection and removal using a convolutional neural network},
author = {J. Calvo-Zaragoza and A. Pertusa and J. Oncina},
issn = {1432-1769},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Machine Vision and Applications},
pages = {1-10},
abstract = {Staff-line removal is an important preprocessing stage for most optical music recognition systems. Common procedures to solve this task involve image processing techniques. In contrast to these traditional methods based on hand-engineered transformations, the problem can also be approached as a classification task in which each pixel is labeled as either staff or symbol, so that only those that belong to symbols are kept in the image. In order to perform this classification, we propose the use of convolutional neural networks, which have demonstrated an outstanding performance in image retrieval tasks. The initial features of each pixel consist of a square patch from the input image centered at that pixel. The proposed network is trained by using a dataset which contains pairs of scores with and without the staff lines. Our results in both binary and grayscale images show that the proposed technique is very accurate, outperforming both other classifiers and the state-of-the-art strategies considered. In addition, several advantages of the presented methodology with respect to traditional procedures proposed so far are discussed.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Staff-line removal is an important preprocessing stage for most optical music recognition systems. Common procedures to solve this task involve image processing techniques. In contrast to these traditional methods based on hand-engineered transformations, the problem can also be approached as a classification task in which each pixel is labeled as either staff or symbol, so that only those that belong to symbols are kept in the image. In order to perform this classification, we propose the use of convolutional neural networks, which have demonstrated an outstanding performance in image retrieval tasks. The initial features of each pixel consist of a square patch from the input image centered at that pixel. The proposed network is trained by using a dataset which contains pairs of scores with and without the staff lines. Our results in both binary and grayscale images show that the proposed technique is very accurate, outperforming both other classifiers and the state-of-the-art strategies considered. In addition, several advantages of the presented methodology with respect to traditional procedures proposed so far are discussed. Valero-Mas, J. J.; Iñesta, J. M.
Interactive User Correction of Automatically Detected Onsets: Approach and Evaluation Journal Article
In: EURASIP Journal on Audio, Speech, and Music Processing, no. 15, 2017, ISSN: 1687-4722.
@article{k367,
title = {Interactive User Correction of Automatically Detected Onsets: Approach and Evaluation},
author = {J. J. Valero-Mas and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/367/EURASIP_Valero-MasInesta-2017.pdf},
issn = {1687-4722},
year = {2017},
date = {2017-01-01},
journal = {EURASIP Journal on Audio, Speech, and Music Processing},
number = {15},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Gallego, A. J.; Calvo-Zaragoza, J.
Staff-line removal with Selectional Auto-Encoders Journal Article
In: Expert Systems With Applications, vol. 89, pp. 138 - 148, 2017, ISSN: 0957-4174.
Abstract | Links | BibTeX | Tags: TIMuL
@article{k372,
title = {Staff-line removal with Selectional Auto-Encoders},
author = {A. J. Gallego and J. Calvo-Zaragoza},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/372/staff-line-removal.pdf},
issn = {0957-4174},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Expert Systems With Applications},
volume = {89},
pages = {138 - 148},
abstract = {Staff-line removal is an important preprocessing stage as regards most Optical Music Recognition systems. The common procedures employed to carry out this task involve image processing techniques. In contrast to these traditional methods, which are based on hand-engineered transformations, the problem can also be approached from a machine learning point of view if representative examples of the task are provided. We propose doing this through the use of a new approach involving auto-encoders, which select the appropriate features of an input feature set (Selectional Auto-Encoders). Within the context of the problem at hand, the model is trained to select those pixels of a given image that belong to a musical symbol, thus removing the lines of the staves. Our results show that the proposed technique is quite competitive and significantly outperforms the other state-of-art strategies considered, particularly when dealing with grayscale input images.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Staff-line removal is an important preprocessing stage as regards most Optical Music Recognition systems. The common procedures employed to carry out this task involve image processing techniques. In contrast to these traditional methods, which are based on hand-engineered transformations, the problem can also be approached from a machine learning point of view if representative examples of the task are provided. We propose doing this through the use of a new approach involving auto-encoders, which select the appropriate features of an input feature set (Selectional Auto-Encoders). Within the context of the problem at hand, the model is trained to select those pixels of a given image that belong to a musical symbol, thus removing the lines of the staves. Our results show that the proposed technique is quite competitive and significantly outperforms the other state-of-art strategies considered, particularly when dealing with grayscale input images. Rizo, D.; Pascual, B.; Iñesta, J. M.; González, L. A.; Ezquerro, A.
Towards the Digital Encoding of Hispanic White Mensural Notation Journal Article
In: Anuario Musical, no. 72, pp. 293–304, 2017, ISSN: 0211-3538.
Abstract | BibTeX | Tags: TIMuL
@article{k383,
title = {Towards the Digital Encoding of Hispanic White Mensural Notation},
author = {D. Rizo and B. Pascual and J. M. Iñesta and L. A. González and A. Ezquerro},
issn = {0211-3538},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Anuario Musical},
number = {72},
pages = {293--304},
abstract = {In this work, the elements necessary for digitally encoding music contained in manuscripts from the centuries 16th to 17th are introduced. The solutions proposed to overcome the difficulties that generate some of the aspects that make this notation different from the modern Western notation are presented. Problems faced are, for example, the absence of bar lines or the duration of notes that are based on the context. The new typographic font 'Capitán', created specifically to represent this type of early notation, is also presented.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
In this work, the elements necessary for digitally encoding music contained in manuscripts from the centuries 16th to 17th are introduced. The solutions proposed to overcome the difficulties that generate some of the aspects that make this notation different from the modern Western notation are presented. Problems faced are, for example, the absence of bar lines or the duration of notes that are based on the context. The new typographic font 'Capitán', created specifically to represent this type of early notation, is also presented.2016
Bellet, A.; Bernabeu, J. F.; Habrard, A.; Sebban, M.
Learning discriminative tree edit similarities for linear classification - Application to melody recognition Journal Article
In: Neurocomputing, vol. 214, pp. 155-161, 2016.
Abstract | Links | BibTeX | Tags: TIMuL
@article{k349,
title = {Learning discriminative tree edit similarities for linear classification - Application to melody recognition},
author = {A. Bellet and J. F. Bernabeu and A. Habrard and M. Sebban},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/349/ldTree2016.pdf},
year = {2016},
date = {2016-11-01},
urldate = {2016-11-01},
journal = {Neurocomputing},
volume = {214},
pages = {155-161},
abstract = {Similarity functions are a fundamental component of many learning algorithms. When dealing with string or tree-structured data, measures based on the edit distance are widely used, and there exist a few methods for learning them from data. In this context, we recently proposed GESL (Bellet et al., 2012 [3]), an approach to string edit similarity learning based on loss minimization which offers theoretical guarantees as to the generalization ability and discriminative power of the learned similarities. In this paper, we argue that GESL, which has been originally dedicated to deal with strings, can be extended to trees and lead to powerful and competitive similarities. We illustrate this claim on a music recognition task, namely melody classification, where each piece is represented as a tree modeling its structure as well as rhythm and pitch information. The results show that GESL outperforms standard as well as probabilistically-learned edit distances and that it is able to describe consistently the underlying melodic similarity model.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Similarity functions are a fundamental component of many learning algorithms. When dealing with string or tree-structured data, measures based on the edit distance are widely used, and there exist a few methods for learning them from data. In this context, we recently proposed GESL (Bellet et al., 2012 [3]), an approach to string edit similarity learning based on loss minimization which offers theoretical guarantees as to the generalization ability and discriminative power of the learned similarities. In this paper, we argue that GESL, which has been originally dedicated to deal with strings, can be extended to trees and lead to powerful and competitive similarities. We illustrate this claim on a music recognition task, namely melody classification, where each piece is represented as a tree modeling its structure as well as rhythm and pitch information. The results show that GESL outperforms standard as well as probabilistically-learned edit distances and that it is able to describe consistently the underlying melodic similarity model. Valero-Mas, J. J.; Benetos, E.; Iñesta, J. M.
Classification-based Note Tracking for Automatic Music Transcription Proceedings Article
In: Proceedings of the 9th Machine Learning and Music Workshop (MML2016), pp. 61–65, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD) Riva del Garda, Italy, 2016.
@inproceedings{k352,
title = {Classification-based Note Tracking for Automatic Music Transcription},
author = {J. J. Valero-Mas and E. Benetos and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/352/ValeroMasBenetosInesta-MML2016.pdf},
year = {2016},
date = {2016-09-01},
booktitle = {Proceedings of the 9th Machine Learning and Music Workshop (MML2016)},
pages = {61--65},
address = {Riva del Garda, Italy},
organization = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD)},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizo, D.; Calvo-Zaragoza, J.; Iñesta, J. M.; Illescas, P. R.
Hidden Markov Models for Functional Analysis Proceedings Article
In: Music and Machine Learning Workshop, Riva del Garda, 2016.
@inproceedings{k370,
title = {Hidden Markov Models for Functional Analysis},
author = {D. Rizo and J. Calvo-Zaragoza and J. M. Iñesta and P. R. Illescas},
year = {2016},
date = {2016-09-01},
booktitle = {Music and Machine Learning Workshop, Riva del Garda},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Valero-Mas, J. J.; Calvo-Zaragoza, J.; Rico-Juan, J. R.
On the suitability of Prototype Selection methods for kNN classification with distributed data Journal Article
In: Neurocomputing, vol. 203, pp. 150-160, 2016.
@article{k341,
title = {On the suitability of Prototype Selection methods for kNN classification with distributed data},
author = {J. J. Valero-Mas and J. Calvo-Zaragoza and J. R. Rico-Juan},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/341/SuitabilityPSDistributedScenarios.pdf},
year = {2016},
date = {2016-08-01},
journal = {Neurocomputing},
volume = {203},
pages = {150-160},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Rizo, D.; Iñesta, J. M.
Two (note) heads are better than one: pen-based multimodal interaction with music scores Proceedings Article
In: Devaney, J. (Ed.): 17th International Society for Music Information Retrieval Conference, pp. 509-514, New York City, 2016, ISBN: 978-0-692-75506-8.
@inproceedings{k345,
title = {Two (note) heads are better than one: pen-based multimodal interaction with music scores},
author = {J. Calvo-Zaragoza and D. Rizo and J. M. Iñesta},
editor = {J. Devaney},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/345/two-note-heads.pdf},
isbn = {978-0-692-75506-8},
year = {2016},
date = {2016-08-01},
booktitle = {17th International Society for Music Information Retrieval Conference},
pages = {509-514},
address = {New York City},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizo, D.; Marsden, A.
A standard format proposal for hierarchical analyses and representations Proceedings Article
In: Proceedings of the 3rd International Workshop on Digital Libraries for Musicology, pp. 25–32, ACM, New York, USA, 2016, ISBN: 978-1-4503-4751-8.
Abstract | BibTeX | Tags: TIMuL
@inproceedings{k356,
title = {A standard format proposal for hierarchical analyses and representations},
author = {D. Rizo and A. Marsden},
isbn = {978-1-4503-4751-8},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of the 3rd International Workshop on Digital Libraries for Musicology},
pages = {25--32},
publisher = {ACM},
address = {New York, USA},
abstract = {In the realm of digital musicology, standardizations efforts to date have mostly concentrated on the representation of music. Anal- yses of music are increasingly being generated or communicated by digital means. We demonstrate that the same arguments for the desirability of standardization in the representation of music apply also to the representation of analyses of music: proper preservation, sharing of data, and facilitation of digital processing. We concen- trate here on analyses which can be described as hierarchical and show that this covers a broad range of existing analytical formats. We propose an extension of MEI (Music Encoding Initiative) to al- low the encoding of analyses unambiguously associated with and aligned to a representation of the music analysed, making use of existing mechanisms within MEI’ and s parent TEI (Text Encoding Ini- tiative) for the representation of trees and graphs.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
In the realm of digital musicology, standardizations efforts to date have mostly concentrated on the representation of music. Anal- yses of music are increasingly being generated or communicated by digital means. We demonstrate that the same arguments for the desirability of standardization in the representation of music apply also to the representation of analyses of music: proper preservation, sharing of data, and facilitation of digital processing. We concen- trate here on analyses which can be described as hierarchical and show that this covers a broad range of existing analytical formats. We propose an extension of MEI (Music Encoding Initiative) to al- low the encoding of analyses unambiguously associated with and aligned to a representation of the music analysed, making use of existing mechanisms within MEI’ and s parent TEI (Text Encoding Ini- tiative) for the representation of trees and graphs. Calvo-Zaragoza, J.; Oncina, J.; Higuera, C. De La
Computing the Expected Edit Distance from a String to a PFA Proceedings Article
In: Han, Yo-Sub; Salomaa, Kai (Ed.): 21st International Conference Implementation and Application of Automata, pp. 39-50, Springer, 2016.
@inproceedings{k342,
title = {Computing the Expected Edit Distance from a String to a PFA},
author = {J. Calvo-Zaragoza and J. Oncina and C. De La Higuera},
editor = {Yo-Sub Han and Kai Salomaa},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/342/distance-string-pfa.pdf},
year = {2016},
date = {2016-07-01},
urldate = {2016-07-01},
booktitle = {21st International Conference Implementation and Application of Automata},
pages = {39-50},
publisher = {Springer},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Iñesta, J. M.; de León, P. J. Ponce
Data-based melody generation through multi-objective evolutionary computation Journal Article
In: Journal of Mathematics and Music, vol. 10, no. 2, pp. 173-192, 2016, ISSN: 1745-9737.
Abstract | Links | BibTeX | Tags: TIMuL
@article{k344,
title = {Data-based melody generation through multi-objective evolutionary computation},
author = {J. M. Iñesta and P. J. Ponce de León},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/344/Data+based+melody+generation+through+multi+objective+evolutionary+computation+%28post-print%29.pdf},
issn = {1745-9737},
year = {2016},
date = {2016-07-01},
urldate = {2016-07-01},
journal = {Journal of Mathematics and Music},
volume = {10},
number = {2},
pages = {173-192},
abstract = {Genetic-based composition algorithms are able to explore an immense space of possibilities, but the main difficulty has always been the implementation of the selection process. In this work, sets of melodies are utilized for training a machine learning approach to compute fitness, based on different metrics. The fitness of a candidate is provided by combining the metrics, but their values can range through different orders of magnitude and evolve in different ways, which makes it hard to combine these criteria. In order to solve this problem, a multi-objective fitness approach is proposed, in which the best individuals are those in the Pareto front of the multi-dimensional fitness space. Melodic trees are also proposed as a data structure for chromosomic representation of melodies and genetic operators are adapted to them. Some experiments have been carried out using a graphical interface prototype that allows one to explore the creative capabilities of the proposed system. An Online Supplement is provided and can be accessed at http://dx.doi.org/10.1080/17459737.2016.1188171, where the reader can find some technical details, information about the data used, generated melodies, and additional information about the developed prototype and its performance.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Genetic-based composition algorithms are able to explore an immense space of possibilities, but the main difficulty has always been the implementation of the selection process. In this work, sets of melodies are utilized for training a machine learning approach to compute fitness, based on different metrics. The fitness of a candidate is provided by combining the metrics, but their values can range through different orders of magnitude and evolve in different ways, which makes it hard to combine these criteria. In order to solve this problem, a multi-objective fitness approach is proposed, in which the best individuals are those in the Pareto front of the multi-dimensional fitness space. Melodic trees are also proposed as a data structure for chromosomic representation of melodies and genetic operators are adapted to them. Some experiments have been carried out using a graphical interface prototype that allows one to explore the creative capabilities of the proposed system. An Online Supplement is provided and can be accessed at http://dx.doi.org/10.1080/17459737.2016.1188171, where the reader can find some technical details, information about the data used, generated melodies, and additional information about the developed prototype and its performance. Bernabeu, M.; Pertusa, A.; Gallego, A. J.
Image spatial verification using Segment Intersection of Interest Points Proceedings Article
In: Proc. of the 24 Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), 2016, ISBN: 2464-4614.
@inproceedings{k346,
title = {Image spatial verification using Segment Intersection of Interest Points},
author = {M. Bernabeu and A. Pertusa and A. J. Gallego},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/346/imageSpatialVerification.pdf},
isbn = {2464-4614},
year = {2016},
date = {2016-05-01},
booktitle = {Proc. of the 24 Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG)},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Illescas, P. R.
Análisis tonal asistido por ordenador PhD Thesis
2016.
Abstract | Links | BibTeX | Tags: TIMuL
@phdthesis{k335,
title = {Análisis tonal asistido por ordenador},
author = {P. R. Illescas},
editor = {J. M. Iñesta and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/335/PhD_placido+illescas-lectura_digital.pdf},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
address = {Alicante},
organization = {Universidad de Alicante},
abstract = {En este trabajo se plantean fundamentalmente cuatro cuestiones de investigación:
1. Realizar unas reseñas sobre la evolución del análisis desde su invención hasta todo lo que se desarrolla entorno al análisis-computacional.
2. Contestar a la cuestión de si es posible (o hasta qué punto) desarrollar reglas armónicas, contrapuntísticas, tonales y funcionales que nos permitan analizar automáticamente los corales armonizados de J. S. Bach.
3. Implementar un programa que en base a las especificaciones producidas en el segundo bloque, analice los corales armonizados de Bach detectando la tonalidad y las modulaciones, los acordes, las funciones tonales y catalogando las notas como reales o extrañas.
4. Explorar las posibilidades de mejorar los resultados producidos por el sistema mediante las interacciones que un experto o estudiante puedan establecer con el mismo, esto abre la puerta a aplicaciones didácticas del sistema.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {phdthesis}
}
En este trabajo se plantean fundamentalmente cuatro cuestiones de investigación:
1. Realizar unas reseñas sobre la evolución del análisis desde su invención hasta todo lo que se desarrolla entorno al análisis-computacional.
2. Contestar a la cuestión de si es posible (o hasta qué punto) desarrollar reglas armónicas, contrapuntísticas, tonales y funcionales que nos permitan analizar automáticamente los corales armonizados de J. S. Bach.
3. Implementar un programa que en base a las especificaciones producidas en el segundo bloque, analice los corales armonizados de Bach detectando la tonalidad y las modulaciones, los acordes, las funciones tonales y catalogando las notas como reales o extrañas.
4. Explorar las posibilidades de mejorar los resultados producidos por el sistema mediante las interacciones que un experto o estudiante puedan establecer con el mismo, esto abre la puerta a aplicaciones didácticas del sistema. Calvo-Zaragoza, J.; Micó, L.; Oncina, J.
Music staff removal with supervised pixel classification Journal Article
In: International Journal on Document Analysis and Recognition, vol. 19, no. 3, pp. 211-219, 2016, ISSN: 1433-2833.
@article{k336,
title = {Music staff removal with supervised pixel classification},
author = {J. Calvo-Zaragoza and L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/336/classification-approach-staff.pdf},
issn = {1433-2833},
year = {2016},
date = {2016-01-01},
journal = {International Journal on Document Analysis and Recognition},
volume = {19},
number = {3},
pages = {211-219},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R.
Prototype Generation on Structural Data using Dissimilarity Space Representation Journal Article
In: Neural Computing and Applications, 2016.
@article{k337,
title = {Prototype Generation on Structural Data using Dissimilarity Space Representation},
author = {J. Calvo-Zaragoza and J. J. Valero-Mas and J. R. Rico-Juan},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/337/prototype-generation-structural.pdf},
year = {2016},
date = {2016-01-01},
journal = {Neural Computing and Applications},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R.
Selecting promising classes from generated data for an efficient multi-class NN classification Journal Article
In: Soft Computing, 2016.
@article{k340,
title = {Selecting promising classes from generated data for an efficient multi-class NN classification},
author = {J. Calvo-Zaragoza and J. J. Valero-Mas and J. R. Rico-Juan},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/340/selecting-promising-classes.pdf},
year = {2016},
date = {2016-01-01},
journal = {Soft Computing},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Toselli, A. H.; Vidal, E.
Early Handwritten Music Recognition with Hidden Markov Models Proceedings Article
In: 15th International Conference on Frontiers in Handwriting Recognition, 2016.
@inproceedings{k350,
title = {Early Handwritten Music Recognition with Hidden Markov Models},
author = {J. Calvo-Zaragoza and A. H. Toselli and E. Vidal},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/350/musicNoteRecogIcfhr16.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {15th International Conference on Frontiers in Handwriting Recognition},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Bosch, V.; Calvo-Zaragoza, J.; Toselli, A. H.; Vidal, E.
Sheet Music Statistical Layout Analysis Proceedings Article
In: 15th International Conference on Frontiers in Handwriting Recognition, 2016.
@inproceedings{k351,
title = {Sheet Music Statistical Layout Analysis},
author = {V. Bosch and J. Calvo-Zaragoza and A. H. Toselli and E. Vidal},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/351/musicSheetLayout.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {15th International Conference on Frontiers in Handwriting Recognition},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizo, D.; Illescas, P. R.; Iñesta, J. M.
Interactive melodic analysis Book Chapter
In: Meredith, D. (Ed.): Computational Music Analysis, Chapter 7, pp. 191-219, Springer, 2016, ISBN: 978-3-319-25931-4.
Abstract | Links | BibTeX | Tags: GRE-12-34, Prometeo 2012, TIMuL
@inbook{k322,
title = {Interactive melodic analysis},
author = {D. Rizo and P. R. Illescas and J. M. Iñesta},
editor = {D. Meredith},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/322/RizoEtAl.pdf},
isbn = {978-3-319-25931-4},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {Computational Music Analysis},
pages = {191-219},
publisher = {Springer},
chapter = {7},
abstract = {Melodic analysis sets the importance and role of each note in a particular harmonic context. Thus, a note is classified as a harmonic tone, when it belongs to the underlying chord, and as a non harmonic tone otherwise, with a number of categories in this latter case. Automatic systems for solving this task are still far from being available, so it must be assumed that in a practical scenario the human expert must correct the system’s output. Interactive systems allow for turning the user into a source of high-quality and high-confidence training data, so on-line ma- chine learning and interactive pattern recognition provide tools that have proven to be very convenient in this context.},
keywords = {GRE-12-34, Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {inbook}
}
Melodic analysis sets the importance and role of each note in a particular harmonic context. Thus, a note is classified as a harmonic tone, when it belongs to the underlying chord, and as a non harmonic tone otherwise, with a number of categories in this latter case. Automatic systems for solving this task are still far from being available, so it must be assumed that in a practical scenario the human expert must correct the system’s output. Interactive systems allow for turning the user into a source of high-quality and high-confidence training data, so on-line ma- chine learning and interactive pattern recognition provide tools that have proven to be very convenient in this context. Rizo, D.; Pascual, B.; Ezquerro, A.; Iñesta, J. M.; González, L. A.
Tipografía y transductor para la transcripción musical interactiva de notación mensural hispánica Proceedings Article
In: Libro de actas de las I jornadas sobre la investigación en los centros superiores de enseñanzas artísticas, pp. 6–23, ISEACV, Valencia, 2016, ISBN: 978-84-608-6758-6.
@inproceedings{k364,
title = {Tipografía y transductor para la transcripción musical interactiva de notación mensural hispánica},
author = {D. Rizo and B. Pascual and A. Ezquerro and J. M. Iñesta and L. A. González},
isbn = {978-84-608-6758-6},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {Libro de actas de las I jornadas sobre la investigación en los centros superiores de enseñanzas artísticas},
pages = {6--23},
publisher = {ISEACV},
address = {Valencia},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
2015
Calvo-Zaragoza, J.; Barbancho, I.; Tardón, L. J.; Barbancho, A. M.
Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation Journal Article
In: Pattern Analysis and Applications, vol. 18, no. 4, pp. 933-943, 2015, ISSN: 1433-7541.
Links | BibTeX | Tags: Prometeo 2012, TIMuL
@article{k318,
title = {Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation},
author = {J. Calvo-Zaragoza and I. Barbancho and L. J. Tardón and A. M. Barbancho},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/318/paaa-jcalvo.pdf},
issn = {1433-7541},
year = {2015},
date = {2015-11-01},
urldate = {2015-11-01},
journal = {Pattern Analysis and Applications},
volume = {18},
number = {4},
pages = {933-943},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {article}
}
Valero-Mas, J. J.; Iñesta, J. M.
Interactive onset detection in audio recordings Technical Report
Málaga, Spain, 2015.
Abstract | Links | BibTeX | Tags: TIMuL
@techreport{k334,
title = {Interactive onset detection in audio recordings},
author = {J. J. Valero-Mas and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/334/OnsetInteraction-LBD.pdf},
year = {2015},
date = {2015-10-01},
booktitle = {Late Breaking/Demo extended abstract, 16th International Society for Music Information Retrieval Conference (ISMIR)},
address = {Málaga, Spain},
organization = {University of Alicante},
abstract = {Onset detection still has room for improvement. State-of-the-art onset detection algorithms achieve good results for a range of applications, but for some situations in which the accuracy is a must, human intervention is required to correct the mistakes committed. In such scheme, accuracy in the result is guaranteed at the expense of the manual correction of all errors. Hence, the issue now lies on finding schemes for efficiently exploiting and reducing that user effort. In this work we present an Interactive Pattern Recognition approach for tackling this issue: using a pre-trained classification-based onset detection algorithm, every time the user corrects an error in the estimation, the system modifies its performance accordingly and recalculates the output. Initial results show that user effort is effectively reduced under our proposal.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {techreport}
}
Onset detection still has room for improvement. State-of-the-art onset detection algorithms achieve good results for a range of applications, but for some situations in which the accuracy is a must, human intervention is required to correct the mistakes committed. In such scheme, accuracy in the result is guaranteed at the expense of the manual correction of all errors. Hence, the issue now lies on finding schemes for efficiently exploiting and reducing that user effort. In this work we present an Interactive Pattern Recognition approach for tackling this issue: using a pre-trained classification-based onset detection algorithm, every time the user corrects an error in the estimation, the system modifies its performance accordingly and recalculates the output. Initial results show that user effort is effectively reduced under our proposal. Calvo-Zaragoza, J.; de León, P. J. Ponce; Iñesta, J. M.; Rizo, D.
Genre-based melody generation through multi-objective genetic algorithms Proceedings Article
In: Proceedings of the 8th Machine Learning and Music workshop (MML 2015), Vancouver (Canada), 2015.
Abstract | BibTeX | Tags: TIMuL
@inproceedings{k332,
title = {Genre-based melody generation through multi-objective genetic algorithms},
author = {J. Calvo-Zaragoza and P. J. Ponce de León and J. M. Iñesta and D. Rizo},
year = {2015},
date = {2015-08-01},
urldate = {2015-08-01},
booktitle = {Proceedings of the 8th Machine Learning and Music workshop (MML 2015)},
address = {Vancouver (Canada)},
abstract = {Genetic-based composition algorithms have the ability to ex- plore an immense space of possibilities but the main di},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Genetic-based composition algorithms have the ability to ex- plore an immense space of possibilities but the main di Valero-Mas, J. J.; Salamon, J.; Gómez, E.
Analyzing the influence of pitch quantization and note segmentation on singing voice alignment in the context of audio-based Query-by-Humming Proceedings Article
In: Proceedings of the 12th Sound and Music Computing Conference (SMC), pp. 371–378, Maynooth, Ireland, 2015, ISBN: 9--7809--92746629.
Abstract | Links | BibTeX | Tags: Prometeo 2012, TIMuL
@inproceedings{k331,
title = {Analyzing the influence of pitch quantization and note segmentation on singing voice alignment in the context of audio-based Query-by-Humming},
author = {J. J. Valero-Mas and J. Salamon and E. Gómez},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/331/QBH_SMC2015_CameraReady.pdf},
isbn = {9--7809--92746629},
year = {2015},
date = {2015-07-01},
booktitle = {Proceedings of the 12th Sound and Music Computing Conference (SMC)},
pages = {371--378},
address = {Maynooth, Ireland},
abstract = {Query-by-Humming (QBH) systems base their operation on aligning the melody sung/hummed by a user with a set of candidate melodies retrieved from music tunes. While MIDI-based QBH builds on the premise of existing annotated transcriptions for any candidate song, audio-based research makes use of melody extraction algorithms for the music tunes. In both cases, a melody abstraction process is required for solving issues commonly found in queries such as key transpositions or tempo deviations. Automatic music transcription is commonly used for this, but due to the reported limitations in state-of-the-art methods for real-world queries, other possibilities should be considered. In this work we explore three different melody representations, ranging from a general time-series one to more musical abstractions, which avoid the automatic transcription step, in the context of an audio-based QBH system. Results show that this abstraction process plays a key role in the overall accuracy of the system, obtaining the best scores when temporal segmentation is dynamically performed in terms of pitch change events in the melodic contour.},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Query-by-Humming (QBH) systems base their operation on aligning the melody sung/hummed by a user with a set of candidate melodies retrieved from music tunes. While MIDI-based QBH builds on the premise of existing annotated transcriptions for any candidate song, audio-based research makes use of melody extraction algorithms for the music tunes. In both cases, a melody abstraction process is required for solving issues commonly found in queries such as key transpositions or tempo deviations. Automatic music transcription is commonly used for this, but due to the reported limitations in state-of-the-art methods for real-world queries, other possibilities should be considered. In this work we explore three different melody representations, ranging from a general time-series one to more musical abstractions, which avoid the automatic transcription step, in the context of an audio-based QBH system. Results show that this abstraction process plays a key role in the overall accuracy of the system, obtaining the best scores when temporal segmentation is dynamically performed in terms of pitch change events in the melodic contour. Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R.
Prototype Generation on Structural Data using Dissimilarity Space Representation: A Case of Study Proceedings Article
In: Paredes, Roberto; Cardoso, Jaime S.; Pardo, Xosé M. (Ed.): 7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), pp. 72-82, Springer, Santiago de Compostela, Spain, 2015, ISBN: 978-3-319-19389-2.
Abstract | Links | BibTeX | Tags: TIMuL
@inproceedings{k325,
title = {Prototype Generation on Structural Data using Dissimilarity Space Representation: A Case of Study},
author = {J. Calvo-Zaragoza and J. J. Valero-Mas and J. R. Rico-Juan},
editor = {Roberto Paredes and Jaime S. Cardoso and Xosé M. Pardo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/325/prototype-generation-structural.pdf},
isbn = {978-3-319-19389-2},
year = {2015},
date = {2015-06-01},
booktitle = {7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)},
pages = {72-82},
publisher = {Springer},
address = {Santiago de Compostela, Spain},
abstract = {Data Reduction techniques are commonly applied in instance-based classification tasks to lower the amount of data to be processed. Prototype Selection (PS) and Prototype Generation (PG) constitute the most representative approaches. These two families differ in the way of obtaining the reduced set out of the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to better delimit decision boundaries, operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs.
This work proposes a case of study with the use of the common RandomC algorithm for mapping the initial structural data to a Dissimilarity Space (DS) representation, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that PG combined with RandomC mapping achieves a very competitive performance, although the obtained accuracy seems to be bounded by the representativity of the DS method.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Data Reduction techniques are commonly applied in instance-based classification tasks to lower the amount of data to be processed. Prototype Selection (PS) and Prototype Generation (PG) constitute the most representative approaches. These two families differ in the way of obtaining the reduced set out of the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to better delimit decision boundaries, operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs.
This work proposes a case of study with the use of the common RandomC algorithm for mapping the initial structural data to a Dissimilarity Space (DS) representation, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that PG combined with RandomC mapping achieves a very competitive performance, although the obtained accuracy seems to be bounded by the representativity of the DS method. Calvo-Zaragoza, J.; Oncina, J.
Clustering of Strokes from Pen-based Music Notation: An Experimental Study Proceedings Article
In: Paredes, Roberto; Cardoso, Jaime S.; Pardo, Xosé M. (Ed.): 7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), pp. 633-640, Springer, Santiago de Compostela, Spain, 2015, ISBN: 978-3-319-19389-2.
Links | BibTeX | Tags: Prometeo 2012, TIMuL
@inproceedings{k326,
title = {Clustering of Strokes from Pen-based Music Notation: An Experimental Study},
author = {J. Calvo-Zaragoza and J. Oncina},
editor = {Roberto Paredes and Jaime S. Cardoso and Xosé M. Pardo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/326/clustering-strokes-pen.pdf},
isbn = {978-3-319-19389-2},
year = {2015},
date = {2015-06-01},
booktitle = {7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)},
pages = {633-640},
publisher = {Springer},
address = {Santiago de Compostela, Spain},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Micó, L.; Sanches, J.; Cardoso, J. S.
The vitality of pattern recognition and image analysis Journal Article
In: Neurocomputing, vol. 150, pp. 124-125, 2015, ISSN: 09252312.
BibTeX | Tags: Prometeo 2012, TIMuL
@article{k320,
title = {The vitality of pattern recognition and image analysis},
author = {L. Micó and J. Sanches and J. S. Cardoso},
issn = {09252312},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Neurocomputing},
volume = {150},
pages = {124-125},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {article}
}
Rizo, D.; Iñesta, J. M.
A grammar for Plaine and Easie Code Proceedings Article
In: Roland, Perry; Kepper, Johannes (Ed.): Proceedings of the Music Encoding Initiative Conferences 2013 and 2014, pp. 54–64, 2015.
BibTeX | Tags: GRE-12-34, Prometeo 2012, TIMuL
@inproceedings{k321,
title = {A grammar for Plaine and Easie Code},
author = {D. Rizo and J. M. Iñesta},
editor = {Perry Roland and Johannes Kepper},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings of the Music Encoding Initiative Conferences 2013 and 2014},
pages = {54--64},
keywords = {GRE-12-34, Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rico-Juan, J. R.; Calvo-Zaragoza, J.
Improving classification using a Confidence Matrix based on weak classifiers applied to OCR Journal Article
In: Neurocomputing, vol. 151, pp. 1354–1361, 2015, ISSN: 0925-2312.
Links | BibTeX | Tags: Prometeo 2012, TIMuL
@article{k323,
title = {Improving classification using a Confidence Matrix based on weak classifiers applied to OCR},
author = {J. R. Rico-Juan and J. Calvo-Zaragoza},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/323/cm.pdf},
issn = {0925-2312},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Neurocomputing},
volume = {151},
pages = {1354–1361},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R
Improving kNN multi-label classification in Prototype Selection scenarios using class proposals Journal Article
In: Pattern Recognition, vol. 48, no. 5, pp. 1608-1622, 2015.
Links | BibTeX | Tags: Prometeo 2012, TIMuL
@article{k324,
title = {Improving kNN multi-label classification in Prototype Selection scenarios using class proposals},
author = {J. Calvo-Zaragoza and J. J. Valero-Mas and J. R Rico-Juan},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/324/improving-knn-multi.pdf},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Pattern Recognition},
volume = {48},
number = {5},
pages = {1608-1622},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {article}
}
2014
Illescas, P. R.; Rizo, D.; Iñesta, J. M.
Melodic analysis of polyphonic music using an interactive pattern recognition tool Proceedings Article
In: Proc. of 7th Machine Learning and Music (MML2014), Barcelona, 2014.
@inproceedings{k328,
title = {Melodic analysis of polyphonic music using an interactive pattern recognition tool},
author = {P. R. Illescas and D. Rizo and J. M. Iñesta},
year = {2014},
date = {2014-12-01},
booktitle = {Proc. of 7th Machine Learning and Music (MML2014)},
address = {Barcelona},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Micó, L.; Oncina, J.
Dynamic Insertions in TLAESA fast NN Search Algorithm Proceedings Article
In: Proceedings of the 22nd International Conference on Pattern Recognition, ICPR, Stockholm, Sweden, 2014, ISBN: 978-1-4799-5208-3.
Abstract | Links | BibTeX | Tags: Prometeo 2012, TIMuL
@inproceedings{k319,
title = {Dynamic Insertions in TLAESA fast NN Search Algorithm},
author = {L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/319/icpr-2014.pdf},
isbn = {978-1-4799-5208-3},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 22nd International Conference on Pattern Recognition, ICPR},
address = {Stockholm, Sweden},
abstract = {Nearest Neighbour search (NNS) is a widely used
technique in Pattern Recognition. In order to speed up the search many indexing techniques have been proposed. The need to work with large dynamic databases in interactive or online systems, has resulted in an increase interest in adapting or
creating fast methods to update these indexes. TLAESA is a fast search algorithm that computes a very low number of distance computations with sublinear overhead using a branch and bound technique.
In this paper, we propose a new fast updating method for the
TLAESA index. The behaviour of this index has been analysed
theoretical and experimentally. We have obtained a log-square
upper bound of the rebuilding expected time. This bound has
been verified experimentally on several synthetic and real data
experiments.},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Nearest Neighbour search (NNS) is a widely used
technique in Pattern Recognition. In order to speed up the search many indexing techniques have been proposed. The need to work with large dynamic databases in interactive or online systems, has resulted in an increase interest in adapting or
creating fast methods to update these indexes. TLAESA is a fast search algorithm that computes a very low number of distance computations with sublinear overhead using a branch and bound technique.
In this paper, we propose a new fast updating method for the
TLAESA index. The behaviour of this index has been analysed
theoretical and experimentally. We have obtained a log-square
upper bound of the rebuilding expected time. This bound has
been verified experimentally on several synthetic and real data
experiments.
2018
Pertusa, A.; Gallego, A. J.; Bernabeu, M.
MirBot: A collaborative object recognition system for smartphones using convolutional neural networks Journal Article
In: Neurocomputing, vol. 293, pp. 87-99, 2018, ISSN: 0925-2312.
Abstract | BibTeX | Tags: TIMuL
@article{k366,
title = {MirBot: A collaborative object recognition system for smartphones using convolutional neural networks},
author = {A. Pertusa and A. J. Gallego and M. Bernabeu},
issn = {0925-2312},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Neurocomputing},
volume = {293},
pages = {87-99},
abstract = {MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neural network (CNN). The answers provided by the system can be validated by the user so as to improve the results for future queries. All the images are stored together with a series of metadata, thus enabling a multimodal incremental dataset labeled with synset identifiers from the WordNet ontology. This dataset grows continuously thanks to the users' feedback, and is publicly available for research. This work details the MirBot object recognition system, analyzes the statistics gathered after more than four years of usage, describes the image classification methodology, and performs an exhaustive evaluation using handcrafted features, neural codes, different transfer learning techniques, PCA compression and metadata, which can be used to improve the image classifier results. The app is freely available at the Apple and Google Play stores.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neural network (CNN). The answers provided by the system can be validated by the user so as to improve the results for future queries. All the images are stored together with a series of metadata, thus enabling a multimodal incremental dataset labeled with synset identifiers from the WordNet ontology. This dataset grows continuously thanks to the users' feedback, and is publicly available for research. This work details the MirBot object recognition system, analyzes the statistics gathered after more than four years of usage, describes the image classification methodology, and performs an exhaustive evaluation using handcrafted features, neural codes, different transfer learning techniques, PCA compression and metadata, which can be used to improve the image classifier results. The app is freely available at the Apple and Google Play stores.
Gallego, A. J.; Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R.
Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation Journal Article
In: Pattern Recognition, vol. 74, pp. 531-543, 2018.
@article{k378,
title = {Clustering-based k-nearest neighbor classification for large-scale data with neural codes representation},
author = {A. J. Gallego and J. Calvo-Zaragoza and J. J. Valero-Mas and J. R. Rico-Juan},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Pattern Recognition},
volume = {74},
pages = {531-543},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Iñesta, J. M.; Conklin, D.; Ramírez, R.; Fiore, T. M. M.
Machine Learning and Music Generation Book
Routledge, Taylor & Francis, 2018, ISBN: 978-0-8153-7720-7.
@book{k384,
title = {Machine Learning and Music Generation},
author = {J. M. Iñesta and D. Conklin and R. Ramírez and T. M. M. Fiore},
editor = {Thomas M. Fiore},
isbn = {978-0-8153-7720-7},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
pages = {111},
publisher = {Routledge, Taylor & Francis},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {book}
}
2017
Calvo-Zaragoza, J.; Gallego, A. J.; Pertusa, A.
Recognition of Handwritten Music Symbols with Convolutional Neural Codes Proceedings Article
In: 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), pp. 691–696, Kyoto, Japan, 2017.
BibTeX | Tags: GRE16-14, TIMuL
@inproceedings{k376,
title = {Recognition of Handwritten Music Symbols with Convolutional Neural Codes},
author = {J. Calvo-Zaragoza and A. J. Gallego and A. Pertusa},
year = {2017},
date = {2017-11-01},
urldate = {2017-11-01},
booktitle = {14th IAPR International Conference on Document Analysis and Recognition (ICDAR)},
pages = {691--696},
address = {Kyoto, Japan},
keywords = {GRE16-14, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Sober-Mira, J.; Calvo-Zaragoza, J.; Rizo, D.; Iñesta, J. M.
Pen-based music document transcription Proceedings Article
In: Proceedings of GREC 2017, pp. 21–22, IEEE computer society, Kyoto (Japan), 2017, ISBN: 978-1-5386-3586-5.
@inproceedings{k381,
title = {Pen-based music document transcription},
author = {J. Sober-Mira and J. Calvo-Zaragoza and D. Rizo and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/381/3586c021.pdf},
isbn = {978-1-5386-3586-5},
year = {2017},
date = {2017-11-01},
booktitle = {Proceedings of GREC 2017},
pages = {21--22},
publisher = {IEEE computer society},
address = {Kyoto (Japan)},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Calvo-Zaragoza, J.; Valero-Mas, J. J.; Pertusa, A
End-To-End Optical Music Recognition using Neural Networks Proceedings Article
In: Proc. of International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China, 2017.
Abstract | BibTeX | Tags: GRE16-14, TIMuL
@inproceedings{k374,
title = {End-To-End Optical Music Recognition using Neural Networks},
author = {J. Calvo-Zaragoza and J. J. Valero-Mas and A Pertusa},
year = {2017},
date = {2017-10-01},
booktitle = {Proc. of International Society for Music Information Retrieval Conference (ISMIR)},
address = {Suzhou, China},
abstract = {This work addresses the Optical Music Recognition (OMR) task in an end-to-end fashion using neural net- works. The proposed architecture is based on a Recurrent Convolutional Neural Network topology that takes as input an image of a monophonic score and retrieves a sequence of music symbols as output. In the first stage, a series of convolutional filters are trained to extract meaningful fea- tures of the input image, and then a recurrent block models the sequential nature of music. The system is trained us- ing a Connectionist Temporal Classification loss function, which avoids the need for a frame-by-frame alignment be- tween the image and the ground-truth music symbols. Ex- perimentation has been carried on a set of 90,000 synthetic monophonic music scores with more than 50 different pos- sible labels. Results obtained depict classification error rates around 2 % at symbol level, thus proving the po- tential of the proposed end-to-end architecture for OMR. The source code, dataset, and trained models are publicly released for reproducible research and future comparison purposes.},
keywords = {GRE16-14, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
This work addresses the Optical Music Recognition (OMR) task in an end-to-end fashion using neural net- works. The proposed architecture is based on a Recurrent Convolutional Neural Network topology that takes as input an image of a monophonic score and retrieves a sequence of music symbols as output. In the first stage, a series of convolutional filters are trained to extract meaningful fea- tures of the input image, and then a recurrent block models the sequential nature of music. The system is trained us- ing a Connectionist Temporal Classification loss function, which avoids the need for a frame-by-frame alignment be- tween the image and the ground-truth music symbols. Ex- perimentation has been carried on a set of 90,000 synthetic monophonic music scores with more than 50 different pos- sible labels. Results obtained depict classification error rates around 2 % at symbol level, thus proving the po- tential of the proposed end-to-end architecture for OMR. The source code, dataset, and trained models are publicly released for reproducible research and future comparison purposes.
Hontanilla, M.; Pérez-Sancho, C.; Iñesta, J. M.
Music style recognition with language models -- beyond statistical results Proceedings Article
In: Proceedings of MML 2017, pp. 31–36, Barcelona, 2017.
@inproceedings{k379,
title = {Music style recognition with language models -- beyond statistical results},
author = {M. Hontanilla and C. Pérez-Sancho and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/379/mml17proceedings-35.pdf},
year = {2017},
date = {2017-10-01},
booktitle = {Proceedings of MML 2017},
pages = {31--36},
address = {Barcelona},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Valero-Mas, J. J.
Towards Interactive Multimodal Music Transcription PhD Thesis
2017.
@phdthesis{k371,
title = {Towards Interactive Multimodal Music Transcription},
author = {J. J. Valero-Mas},
editor = {José M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/371/Thesis.pdf},
year = {2017},
date = {2017-07-01},
urldate = {2017-07-01},
organization = {University of Alicante},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {phdthesis}
}
Bernabeu, J. F.
Similarity Learning and Stochastic Language Models for Tree-Represented Music PhD Thesis
2017.
@phdthesis{k377,
title = {Similarity Learning and Stochastic Language Models for Tree-Represented Music},
author = {J. F. Bernabeu},
editor = {José M. Iñesta and J. Calera-Rubio},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/377/BernabeuPhD.pdf},
year = {2017},
date = {2017-07-01},
organization = {University of Alicante},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {phdthesis}
}
Valero-Mas, J. J.; Benetos, E.; Iñesta, J. M.
Assessing the Relevance of Onset Information for Note Tracking in Piano Music Transcription Proceedings Article
In: Proceedings of the AES International Conference on Semantic Audio, Eerlangen, 2017.
@inproceedings{k363,
title = {Assessing the Relevance of Onset Information for Note Tracking in Piano Music Transcription},
author = {J. J. Valero-Mas and E. Benetos and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/363/CameraReady.pdf},
year = {2017},
date = {2017-06-21},
booktitle = {Proceedings of the AES International Conference on Semantic Audio},
address = {Eerlangen},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Valero-Mas, J. J.; Calvo-Zaragoza, J.; Rico-Juan, J. R.; Iñesta, J. M.
A study of Prototype Selection algorithms for Nearest Neighbour in class-imbalanced problems Proceedings Article
In: Alexandre, J. S. Sánchez L. A.; Rodrigues, J. M. F. (Ed.): Proceedings of the 8th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), pp. 335–343, Springer, Faro, Portugal, 2017, ISBN: 978-3-319-58837-7.
@inproceedings{k362,
title = {A study of Prototype Selection algorithms for Nearest Neighbour in class-imbalanced problems},
author = {J. J. Valero-Mas and J. Calvo-Zaragoza and J. R. Rico-Juan and J. M. Iñesta},
editor = {J. S. Sánchez L. A. Alexandre and J. M. F. Rodrigues},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/362/CameraReady.pdf},
isbn = {978-3-319-58837-7},
year = {2017},
date = {2017-06-01},
booktitle = {Proceedings of the 8th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)},
pages = {335--343},
publisher = {Springer},
address = {Faro, Portugal},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizo, D.; Calvo-Zaragoza, J.; Iñesta, J. M.; Fujinaga, I.
About agnostic representation of musical documents for Optical Music Recognition Proceedings Article
In: Music Encoding Conference, Tours, 2017, 2017.
@inproceedings{k369,
title = {About agnostic representation of musical documents for Optical Music Recognition},
author = {D. Rizo and J. Calvo-Zaragoza and J. M. Iñesta and I. Fujinaga},
year = {2017},
date = {2017-05-01},
booktitle = {Music Encoding Conference, Tours, 2017},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Calvo-Zaragoza, J.; Oncina, J.
Recognition of Pen-based Music Notation with Finite-State Machines Journal Article
In: Expert Systems With Applications, vol. 72, pp. 395-406, 2017.
@article{k358,
title = {Recognition of Pen-based Music Notation with Finite-State Machines},
author = {J. Calvo-Zaragoza and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/358/recognition-pen-based.pdf},
year = {2017},
date = {2017-04-01},
journal = {Expert Systems With Applications},
volume = {72},
pages = {395-406},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Oncina, J.
An efficient approach for Interactive Sequential Pattern Recognition Journal Article
In: Pattern Recognition, vol. 64, pp. 295-304, 2017.
@article{k359,
title = {An efficient approach for Interactive Sequential Pattern Recognition},
author = {J. Calvo-Zaragoza and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/359/efficient-approach-sequential.pdf},
year = {2017},
date = {2017-04-01},
journal = {Pattern Recognition},
volume = {64},
pages = {295-304},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Valero-Mas, J. J.; Calvo-Zaragoza, J.; Rico-Juan, J. R.; Iñesta, J. M.
An Experimental Study on Rank Methods for Prototype Selection Journal Article
In: Soft Computing, vol. 21, no. 19, pp. 5703-–5715, 2017, ISSN: 1432-7643.
Abstract | Links | BibTeX | Tags: TIMuL
@article{k339,
title = {An Experimental Study on Rank Methods for Prototype Selection},
author = {J. J. Valero-Mas and J. Calvo-Zaragoza and J. R. Rico-Juan and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/339/SOCO-RankMethods-2016.pdf},
issn = {1432-7643},
year = {2017},
date = {2017-01-01},
journal = {Soft Computing},
volume = {21},
number = {19},
pages = {5703-–5715},
abstract = {Prototype selection is one of the most popular approaches for addressing the low efficiency issue typically found in the well-known k-Nearest Neighbour classification rule. These techniques select a representative subset from an original collection of prototypes with the premise of main- taining the same classification accuracy. Most recently, rank methods have been proposed as an alternative to develop new selection strategies. Following a certain heuristic, these methods sort the elements of the initial collection accord- ing to their relevance and then select the best possible subset by means of a parameter representing the amount of data to maintain. Due to the relative novelty of these methods, their performance and competitiveness against other strategies is still unclear. This work performs an exhaustive experimental study of such methods for prototype selection. A represen- tative collection of both classic and sophisticated algorithms are compared to the aforementioned techniques in a num- ber of datasets, including different levels of induced noise. Results report the remarkable competitiveness of these rank methods as well as their excellent trade-off between proto- type reduction and achieved accuracy.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Prototype selection is one of the most popular approaches for addressing the low efficiency issue typically found in the well-known k-Nearest Neighbour classification rule. These techniques select a representative subset from an original collection of prototypes with the premise of main- taining the same classification accuracy. Most recently, rank methods have been proposed as an alternative to develop new selection strategies. Following a certain heuristic, these methods sort the elements of the initial collection accord- ing to their relevance and then select the best possible subset by means of a parameter representing the amount of data to maintain. Due to the relative novelty of these methods, their performance and competitiveness against other strategies is still unclear. This work performs an exhaustive experimental study of such methods for prototype selection. A represen- tative collection of both classic and sophisticated algorithms are compared to the aforementioned techniques in a num- ber of datasets, including different levels of induced noise. Results report the remarkable competitiveness of these rank methods as well as their excellent trade-off between proto- type reduction and achieved accuracy.
Calvo-Zaragoza, J.; Vigliensoni, G.; Fujinaga, I.
A machine learning framework for the categorization of elements in images of musical documents Proceedings Article
In: Proceedings of the Third International Conference on Technologies for Music Notation and Representation, 2017.
@inproceedings{k360,
title = {A machine learning framework for the categorization of elements in images of musical documents},
author = {J. Calvo-Zaragoza and G. Vigliensoni and I. Fujinaga},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/360/tenor-unified-categorization.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the Third International Conference on Technologies for Music Notation and Representation},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Calvo-Zaragoza, J.; Vigliensoni, G.; Fujinaga, I.
Pixel-wise Binarization of Musical Documents with Convolutional Neural Networks Proceedings Article
In: Proceedings of the 15th IAPR International Conference on Machine Vision Applications, 2017.
@inproceedings{k361,
title = {Pixel-wise Binarization of Musical Documents with Convolutional Neural Networks},
author = {J. Calvo-Zaragoza and G. Vigliensoni and I. Fujinaga},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/361/pixel-wise-binarization.pdf},
year = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the 15th IAPR International Conference on Machine Vision Applications},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Calvo-Zaragoza, J.; Pertusa, A.; Oncina, J.
Staff-line detection and removal using a convolutional neural network Journal Article
In: Machine Vision and Applications, pp. 1-10, 2017, ISSN: 1432-1769.
Abstract | BibTeX | Tags: TIMuL
@article{k365,
title = {Staff-line detection and removal using a convolutional neural network},
author = {J. Calvo-Zaragoza and A. Pertusa and J. Oncina},
issn = {1432-1769},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Machine Vision and Applications},
pages = {1-10},
abstract = {Staff-line removal is an important preprocessing stage for most optical music recognition systems. Common procedures to solve this task involve image processing techniques. In contrast to these traditional methods based on hand-engineered transformations, the problem can also be approached as a classification task in which each pixel is labeled as either staff or symbol, so that only those that belong to symbols are kept in the image. In order to perform this classification, we propose the use of convolutional neural networks, which have demonstrated an outstanding performance in image retrieval tasks. The initial features of each pixel consist of a square patch from the input image centered at that pixel. The proposed network is trained by using a dataset which contains pairs of scores with and without the staff lines. Our results in both binary and grayscale images show that the proposed technique is very accurate, outperforming both other classifiers and the state-of-the-art strategies considered. In addition, several advantages of the presented methodology with respect to traditional procedures proposed so far are discussed.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Staff-line removal is an important preprocessing stage for most optical music recognition systems. Common procedures to solve this task involve image processing techniques. In contrast to these traditional methods based on hand-engineered transformations, the problem can also be approached as a classification task in which each pixel is labeled as either staff or symbol, so that only those that belong to symbols are kept in the image. In order to perform this classification, we propose the use of convolutional neural networks, which have demonstrated an outstanding performance in image retrieval tasks. The initial features of each pixel consist of a square patch from the input image centered at that pixel. The proposed network is trained by using a dataset which contains pairs of scores with and without the staff lines. Our results in both binary and grayscale images show that the proposed technique is very accurate, outperforming both other classifiers and the state-of-the-art strategies considered. In addition, several advantages of the presented methodology with respect to traditional procedures proposed so far are discussed.
Valero-Mas, J. J.; Iñesta, J. M.
Interactive User Correction of Automatically Detected Onsets: Approach and Evaluation Journal Article
In: EURASIP Journal on Audio, Speech, and Music Processing, no. 15, 2017, ISSN: 1687-4722.
@article{k367,
title = {Interactive User Correction of Automatically Detected Onsets: Approach and Evaluation},
author = {J. J. Valero-Mas and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/367/EURASIP_Valero-MasInesta-2017.pdf},
issn = {1687-4722},
year = {2017},
date = {2017-01-01},
journal = {EURASIP Journal on Audio, Speech, and Music Processing},
number = {15},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Gallego, A. J.; Calvo-Zaragoza, J.
Staff-line removal with Selectional Auto-Encoders Journal Article
In: Expert Systems With Applications, vol. 89, pp. 138 - 148, 2017, ISSN: 0957-4174.
Abstract | Links | BibTeX | Tags: TIMuL
@article{k372,
title = {Staff-line removal with Selectional Auto-Encoders},
author = {A. J. Gallego and J. Calvo-Zaragoza},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/372/staff-line-removal.pdf},
issn = {0957-4174},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Expert Systems With Applications},
volume = {89},
pages = {138 - 148},
abstract = {Staff-line removal is an important preprocessing stage as regards most Optical Music Recognition systems. The common procedures employed to carry out this task involve image processing techniques. In contrast to these traditional methods, which are based on hand-engineered transformations, the problem can also be approached from a machine learning point of view if representative examples of the task are provided. We propose doing this through the use of a new approach involving auto-encoders, which select the appropriate features of an input feature set (Selectional Auto-Encoders). Within the context of the problem at hand, the model is trained to select those pixels of a given image that belong to a musical symbol, thus removing the lines of the staves. Our results show that the proposed technique is quite competitive and significantly outperforms the other state-of-art strategies considered, particularly when dealing with grayscale input images.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Staff-line removal is an important preprocessing stage as regards most Optical Music Recognition systems. The common procedures employed to carry out this task involve image processing techniques. In contrast to these traditional methods, which are based on hand-engineered transformations, the problem can also be approached from a machine learning point of view if representative examples of the task are provided. We propose doing this through the use of a new approach involving auto-encoders, which select the appropriate features of an input feature set (Selectional Auto-Encoders). Within the context of the problem at hand, the model is trained to select those pixels of a given image that belong to a musical symbol, thus removing the lines of the staves. Our results show that the proposed technique is quite competitive and significantly outperforms the other state-of-art strategies considered, particularly when dealing with grayscale input images.
Rizo, D.; Pascual, B.; Iñesta, J. M.; González, L. A.; Ezquerro, A.
Towards the Digital Encoding of Hispanic White Mensural Notation Journal Article
In: Anuario Musical, no. 72, pp. 293–304, 2017, ISSN: 0211-3538.
Abstract | BibTeX | Tags: TIMuL
@article{k383,
title = {Towards the Digital Encoding of Hispanic White Mensural Notation},
author = {D. Rizo and B. Pascual and J. M. Iñesta and L. A. González and A. Ezquerro},
issn = {0211-3538},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Anuario Musical},
number = {72},
pages = {293--304},
abstract = {In this work, the elements necessary for digitally encoding music contained in manuscripts from the centuries 16th to 17th are introduced. The solutions proposed to overcome the difficulties that generate some of the aspects that make this notation different from the modern Western notation are presented. Problems faced are, for example, the absence of bar lines or the duration of notes that are based on the context. The new typographic font 'Capitán', created specifically to represent this type of early notation, is also presented.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
In this work, the elements necessary for digitally encoding music contained in manuscripts from the centuries 16th to 17th are introduced. The solutions proposed to overcome the difficulties that generate some of the aspects that make this notation different from the modern Western notation are presented. Problems faced are, for example, the absence of bar lines or the duration of notes that are based on the context. The new typographic font 'Capitán', created specifically to represent this type of early notation, is also presented.
2016
Bellet, A.; Bernabeu, J. F.; Habrard, A.; Sebban, M.
Learning discriminative tree edit similarities for linear classification - Application to melody recognition Journal Article
In: Neurocomputing, vol. 214, pp. 155-161, 2016.
Abstract | Links | BibTeX | Tags: TIMuL
@article{k349,
title = {Learning discriminative tree edit similarities for linear classification - Application to melody recognition},
author = {A. Bellet and J. F. Bernabeu and A. Habrard and M. Sebban},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/349/ldTree2016.pdf},
year = {2016},
date = {2016-11-01},
urldate = {2016-11-01},
journal = {Neurocomputing},
volume = {214},
pages = {155-161},
abstract = {Similarity functions are a fundamental component of many learning algorithms. When dealing with string or tree-structured data, measures based on the edit distance are widely used, and there exist a few methods for learning them from data. In this context, we recently proposed GESL (Bellet et al., 2012 [3]), an approach to string edit similarity learning based on loss minimization which offers theoretical guarantees as to the generalization ability and discriminative power of the learned similarities. In this paper, we argue that GESL, which has been originally dedicated to deal with strings, can be extended to trees and lead to powerful and competitive similarities. We illustrate this claim on a music recognition task, namely melody classification, where each piece is represented as a tree modeling its structure as well as rhythm and pitch information. The results show that GESL outperforms standard as well as probabilistically-learned edit distances and that it is able to describe consistently the underlying melodic similarity model.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Similarity functions are a fundamental component of many learning algorithms. When dealing with string or tree-structured data, measures based on the edit distance are widely used, and there exist a few methods for learning them from data. In this context, we recently proposed GESL (Bellet et al., 2012 [3]), an approach to string edit similarity learning based on loss minimization which offers theoretical guarantees as to the generalization ability and discriminative power of the learned similarities. In this paper, we argue that GESL, which has been originally dedicated to deal with strings, can be extended to trees and lead to powerful and competitive similarities. We illustrate this claim on a music recognition task, namely melody classification, where each piece is represented as a tree modeling its structure as well as rhythm and pitch information. The results show that GESL outperforms standard as well as probabilistically-learned edit distances and that it is able to describe consistently the underlying melodic similarity model.
Valero-Mas, J. J.; Benetos, E.; Iñesta, J. M.
Classification-based Note Tracking for Automatic Music Transcription Proceedings Article
In: Proceedings of the 9th Machine Learning and Music Workshop (MML2016), pp. 61–65, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD) Riva del Garda, Italy, 2016.
@inproceedings{k352,
title = {Classification-based Note Tracking for Automatic Music Transcription},
author = {J. J. Valero-Mas and E. Benetos and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/352/ValeroMasBenetosInesta-MML2016.pdf},
year = {2016},
date = {2016-09-01},
booktitle = {Proceedings of the 9th Machine Learning and Music Workshop (MML2016)},
pages = {61--65},
address = {Riva del Garda, Italy},
organization = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD)},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizo, D.; Calvo-Zaragoza, J.; Iñesta, J. M.; Illescas, P. R.
Hidden Markov Models for Functional Analysis Proceedings Article
In: Music and Machine Learning Workshop, Riva del Garda, 2016.
@inproceedings{k370,
title = {Hidden Markov Models for Functional Analysis},
author = {D. Rizo and J. Calvo-Zaragoza and J. M. Iñesta and P. R. Illescas},
year = {2016},
date = {2016-09-01},
booktitle = {Music and Machine Learning Workshop, Riva del Garda},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Valero-Mas, J. J.; Calvo-Zaragoza, J.; Rico-Juan, J. R.
On the suitability of Prototype Selection methods for kNN classification with distributed data Journal Article
In: Neurocomputing, vol. 203, pp. 150-160, 2016.
@article{k341,
title = {On the suitability of Prototype Selection methods for kNN classification with distributed data},
author = {J. J. Valero-Mas and J. Calvo-Zaragoza and J. R. Rico-Juan},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/341/SuitabilityPSDistributedScenarios.pdf},
year = {2016},
date = {2016-08-01},
journal = {Neurocomputing},
volume = {203},
pages = {150-160},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Rizo, D.; Iñesta, J. M.
Two (note) heads are better than one: pen-based multimodal interaction with music scores Proceedings Article
In: Devaney, J. (Ed.): 17th International Society for Music Information Retrieval Conference, pp. 509-514, New York City, 2016, ISBN: 978-0-692-75506-8.
@inproceedings{k345,
title = {Two (note) heads are better than one: pen-based multimodal interaction with music scores},
author = {J. Calvo-Zaragoza and D. Rizo and J. M. Iñesta},
editor = {J. Devaney},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/345/two-note-heads.pdf},
isbn = {978-0-692-75506-8},
year = {2016},
date = {2016-08-01},
booktitle = {17th International Society for Music Information Retrieval Conference},
pages = {509-514},
address = {New York City},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizo, D.; Marsden, A.
A standard format proposal for hierarchical analyses and representations Proceedings Article
In: Proceedings of the 3rd International Workshop on Digital Libraries for Musicology, pp. 25–32, ACM, New York, USA, 2016, ISBN: 978-1-4503-4751-8.
Abstract | BibTeX | Tags: TIMuL
@inproceedings{k356,
title = {A standard format proposal for hierarchical analyses and representations},
author = {D. Rizo and A. Marsden},
isbn = {978-1-4503-4751-8},
year = {2016},
date = {2016-08-01},
booktitle = {Proceedings of the 3rd International Workshop on Digital Libraries for Musicology},
pages = {25--32},
publisher = {ACM},
address = {New York, USA},
abstract = {In the realm of digital musicology, standardizations efforts to date have mostly concentrated on the representation of music. Anal- yses of music are increasingly being generated or communicated by digital means. We demonstrate that the same arguments for the desirability of standardization in the representation of music apply also to the representation of analyses of music: proper preservation, sharing of data, and facilitation of digital processing. We concen- trate here on analyses which can be described as hierarchical and show that this covers a broad range of existing analytical formats. We propose an extension of MEI (Music Encoding Initiative) to al- low the encoding of analyses unambiguously associated with and aligned to a representation of the music analysed, making use of existing mechanisms within MEI’ and s parent TEI (Text Encoding Ini- tiative) for the representation of trees and graphs.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
In the realm of digital musicology, standardizations efforts to date have mostly concentrated on the representation of music. Anal- yses of music are increasingly being generated or communicated by digital means. We demonstrate that the same arguments for the desirability of standardization in the representation of music apply also to the representation of analyses of music: proper preservation, sharing of data, and facilitation of digital processing. We concen- trate here on analyses which can be described as hierarchical and show that this covers a broad range of existing analytical formats. We propose an extension of MEI (Music Encoding Initiative) to al- low the encoding of analyses unambiguously associated with and aligned to a representation of the music analysed, making use of existing mechanisms within MEI’ and s parent TEI (Text Encoding Ini- tiative) for the representation of trees and graphs.
Calvo-Zaragoza, J.; Oncina, J.; Higuera, C. De La
Computing the Expected Edit Distance from a String to a PFA Proceedings Article
In: Han, Yo-Sub; Salomaa, Kai (Ed.): 21st International Conference Implementation and Application of Automata, pp. 39-50, Springer, 2016.
@inproceedings{k342,
title = {Computing the Expected Edit Distance from a String to a PFA},
author = {J. Calvo-Zaragoza and J. Oncina and C. De La Higuera},
editor = {Yo-Sub Han and Kai Salomaa},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/342/distance-string-pfa.pdf},
year = {2016},
date = {2016-07-01},
urldate = {2016-07-01},
booktitle = {21st International Conference Implementation and Application of Automata},
pages = {39-50},
publisher = {Springer},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Iñesta, J. M.; de León, P. J. Ponce
Data-based melody generation through multi-objective evolutionary computation Journal Article
In: Journal of Mathematics and Music, vol. 10, no. 2, pp. 173-192, 2016, ISSN: 1745-9737.
Abstract | Links | BibTeX | Tags: TIMuL
@article{k344,
title = {Data-based melody generation through multi-objective evolutionary computation},
author = {J. M. Iñesta and P. J. Ponce de León},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/344/Data+based+melody+generation+through+multi+objective+evolutionary+computation+%28post-print%29.pdf},
issn = {1745-9737},
year = {2016},
date = {2016-07-01},
urldate = {2016-07-01},
journal = {Journal of Mathematics and Music},
volume = {10},
number = {2},
pages = {173-192},
abstract = {Genetic-based composition algorithms are able to explore an immense space of possibilities, but the main difficulty has always been the implementation of the selection process. In this work, sets of melodies are utilized for training a machine learning approach to compute fitness, based on different metrics. The fitness of a candidate is provided by combining the metrics, but their values can range through different orders of magnitude and evolve in different ways, which makes it hard to combine these criteria. In order to solve this problem, a multi-objective fitness approach is proposed, in which the best individuals are those in the Pareto front of the multi-dimensional fitness space. Melodic trees are also proposed as a data structure for chromosomic representation of melodies and genetic operators are adapted to them. Some experiments have been carried out using a graphical interface prototype that allows one to explore the creative capabilities of the proposed system. An Online Supplement is provided and can be accessed at http://dx.doi.org/10.1080/17459737.2016.1188171, where the reader can find some technical details, information about the data used, generated melodies, and additional information about the developed prototype and its performance.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Genetic-based composition algorithms are able to explore an immense space of possibilities, but the main difficulty has always been the implementation of the selection process. In this work, sets of melodies are utilized for training a machine learning approach to compute fitness, based on different metrics. The fitness of a candidate is provided by combining the metrics, but their values can range through different orders of magnitude and evolve in different ways, which makes it hard to combine these criteria. In order to solve this problem, a multi-objective fitness approach is proposed, in which the best individuals are those in the Pareto front of the multi-dimensional fitness space. Melodic trees are also proposed as a data structure for chromosomic representation of melodies and genetic operators are adapted to them. Some experiments have been carried out using a graphical interface prototype that allows one to explore the creative capabilities of the proposed system. An Online Supplement is provided and can be accessed at http://dx.doi.org/10.1080/17459737.2016.1188171, where the reader can find some technical details, information about the data used, generated melodies, and additional information about the developed prototype and its performance.
Bernabeu, M.; Pertusa, A.; Gallego, A. J.
Image spatial verification using Segment Intersection of Interest Points Proceedings Article
In: Proc. of the 24 Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), 2016, ISBN: 2464-4614.
@inproceedings{k346,
title = {Image spatial verification using Segment Intersection of Interest Points},
author = {M. Bernabeu and A. Pertusa and A. J. Gallego},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/346/imageSpatialVerification.pdf},
isbn = {2464-4614},
year = {2016},
date = {2016-05-01},
booktitle = {Proc. of the 24 Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG)},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Illescas, P. R.
Análisis tonal asistido por ordenador PhD Thesis
2016.
Abstract | Links | BibTeX | Tags: TIMuL
@phdthesis{k335,
title = {Análisis tonal asistido por ordenador},
author = {P. R. Illescas},
editor = {J. M. Iñesta and D. Rizo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/335/PhD_placido+illescas-lectura_digital.pdf},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
address = {Alicante},
organization = {Universidad de Alicante},
abstract = {En este trabajo se plantean fundamentalmente cuatro cuestiones de investigación:
1. Realizar unas reseñas sobre la evolución del análisis desde su invención hasta todo lo que se desarrolla entorno al análisis-computacional.
2. Contestar a la cuestión de si es posible (o hasta qué punto) desarrollar reglas armónicas, contrapuntísticas, tonales y funcionales que nos permitan analizar automáticamente los corales armonizados de J. S. Bach.
3. Implementar un programa que en base a las especificaciones producidas en el segundo bloque, analice los corales armonizados de Bach detectando la tonalidad y las modulaciones, los acordes, las funciones tonales y catalogando las notas como reales o extrañas.
4. Explorar las posibilidades de mejorar los resultados producidos por el sistema mediante las interacciones que un experto o estudiante puedan establecer con el mismo, esto abre la puerta a aplicaciones didácticas del sistema.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {phdthesis}
}
En este trabajo se plantean fundamentalmente cuatro cuestiones de investigación:
1. Realizar unas reseñas sobre la evolución del análisis desde su invención hasta todo lo que se desarrolla entorno al análisis-computacional.
2. Contestar a la cuestión de si es posible (o hasta qué punto) desarrollar reglas armónicas, contrapuntísticas, tonales y funcionales que nos permitan analizar automáticamente los corales armonizados de J. S. Bach.
3. Implementar un programa que en base a las especificaciones producidas en el segundo bloque, analice los corales armonizados de Bach detectando la tonalidad y las modulaciones, los acordes, las funciones tonales y catalogando las notas como reales o extrañas.
4. Explorar las posibilidades de mejorar los resultados producidos por el sistema mediante las interacciones que un experto o estudiante puedan establecer con el mismo, esto abre la puerta a aplicaciones didácticas del sistema.
1. Realizar unas reseñas sobre la evolución del análisis desde su invención hasta todo lo que se desarrolla entorno al análisis-computacional.
2. Contestar a la cuestión de si es posible (o hasta qué punto) desarrollar reglas armónicas, contrapuntísticas, tonales y funcionales que nos permitan analizar automáticamente los corales armonizados de J. S. Bach.
3. Implementar un programa que en base a las especificaciones producidas en el segundo bloque, analice los corales armonizados de Bach detectando la tonalidad y las modulaciones, los acordes, las funciones tonales y catalogando las notas como reales o extrañas.
4. Explorar las posibilidades de mejorar los resultados producidos por el sistema mediante las interacciones que un experto o estudiante puedan establecer con el mismo, esto abre la puerta a aplicaciones didácticas del sistema.
Calvo-Zaragoza, J.; Micó, L.; Oncina, J.
Music staff removal with supervised pixel classification Journal Article
In: International Journal on Document Analysis and Recognition, vol. 19, no. 3, pp. 211-219, 2016, ISSN: 1433-2833.
@article{k336,
title = {Music staff removal with supervised pixel classification},
author = {J. Calvo-Zaragoza and L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/336/classification-approach-staff.pdf},
issn = {1433-2833},
year = {2016},
date = {2016-01-01},
journal = {International Journal on Document Analysis and Recognition},
volume = {19},
number = {3},
pages = {211-219},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R.
Prototype Generation on Structural Data using Dissimilarity Space Representation Journal Article
In: Neural Computing and Applications, 2016.
@article{k337,
title = {Prototype Generation on Structural Data using Dissimilarity Space Representation},
author = {J. Calvo-Zaragoza and J. J. Valero-Mas and J. R. Rico-Juan},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/337/prototype-generation-structural.pdf},
year = {2016},
date = {2016-01-01},
journal = {Neural Computing and Applications},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R.
Selecting promising classes from generated data for an efficient multi-class NN classification Journal Article
In: Soft Computing, 2016.
@article{k340,
title = {Selecting promising classes from generated data for an efficient multi-class NN classification},
author = {J. Calvo-Zaragoza and J. J. Valero-Mas and J. R. Rico-Juan},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/340/selecting-promising-classes.pdf},
year = {2016},
date = {2016-01-01},
journal = {Soft Computing},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Toselli, A. H.; Vidal, E.
Early Handwritten Music Recognition with Hidden Markov Models Proceedings Article
In: 15th International Conference on Frontiers in Handwriting Recognition, 2016.
@inproceedings{k350,
title = {Early Handwritten Music Recognition with Hidden Markov Models},
author = {J. Calvo-Zaragoza and A. H. Toselli and E. Vidal},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/350/musicNoteRecogIcfhr16.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {15th International Conference on Frontiers in Handwriting Recognition},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Bosch, V.; Calvo-Zaragoza, J.; Toselli, A. H.; Vidal, E.
Sheet Music Statistical Layout Analysis Proceedings Article
In: 15th International Conference on Frontiers in Handwriting Recognition, 2016.
@inproceedings{k351,
title = {Sheet Music Statistical Layout Analysis},
author = {V. Bosch and J. Calvo-Zaragoza and A. H. Toselli and E. Vidal},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/351/musicSheetLayout.pdf},
year = {2016},
date = {2016-01-01},
booktitle = {15th International Conference on Frontiers in Handwriting Recognition},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rizo, D.; Illescas, P. R.; Iñesta, J. M.
Interactive melodic analysis Book Chapter
In: Meredith, D. (Ed.): Computational Music Analysis, Chapter 7, pp. 191-219, Springer, 2016, ISBN: 978-3-319-25931-4.
Abstract | Links | BibTeX | Tags: GRE-12-34, Prometeo 2012, TIMuL
@inbook{k322,
title = {Interactive melodic analysis},
author = {D. Rizo and P. R. Illescas and J. M. Iñesta},
editor = {D. Meredith},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/322/RizoEtAl.pdf},
isbn = {978-3-319-25931-4},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {Computational Music Analysis},
pages = {191-219},
publisher = {Springer},
chapter = {7},
abstract = {Melodic analysis sets the importance and role of each note in a particular harmonic context. Thus, a note is classified as a harmonic tone, when it belongs to the underlying chord, and as a non harmonic tone otherwise, with a number of categories in this latter case. Automatic systems for solving this task are still far from being available, so it must be assumed that in a practical scenario the human expert must correct the system’s output. Interactive systems allow for turning the user into a source of high-quality and high-confidence training data, so on-line ma- chine learning and interactive pattern recognition provide tools that have proven to be very convenient in this context.},
keywords = {GRE-12-34, Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {inbook}
}
Melodic analysis sets the importance and role of each note in a particular harmonic context. Thus, a note is classified as a harmonic tone, when it belongs to the underlying chord, and as a non harmonic tone otherwise, with a number of categories in this latter case. Automatic systems for solving this task are still far from being available, so it must be assumed that in a practical scenario the human expert must correct the system’s output. Interactive systems allow for turning the user into a source of high-quality and high-confidence training data, so on-line ma- chine learning and interactive pattern recognition provide tools that have proven to be very convenient in this context.
Rizo, D.; Pascual, B.; Ezquerro, A.; Iñesta, J. M.; González, L. A.
Tipografía y transductor para la transcripción musical interactiva de notación mensural hispánica Proceedings Article
In: Libro de actas de las I jornadas sobre la investigación en los centros superiores de enseñanzas artísticas, pp. 6–23, ISEACV, Valencia, 2016, ISBN: 978-84-608-6758-6.
@inproceedings{k364,
title = {Tipografía y transductor para la transcripción musical interactiva de notación mensural hispánica},
author = {D. Rizo and B. Pascual and A. Ezquerro and J. M. Iñesta and L. A. González},
isbn = {978-84-608-6758-6},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
booktitle = {Libro de actas de las I jornadas sobre la investigación en los centros superiores de enseñanzas artísticas},
pages = {6--23},
publisher = {ISEACV},
address = {Valencia},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
2015
Calvo-Zaragoza, J.; Barbancho, I.; Tardón, L. J.; Barbancho, A. M.
Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation Journal Article
In: Pattern Analysis and Applications, vol. 18, no. 4, pp. 933-943, 2015, ISSN: 1433-7541.
Links | BibTeX | Tags: Prometeo 2012, TIMuL
@article{k318,
title = {Avoiding staff removal stage in optical music recognition: application to scores written in white mensural notation},
author = {J. Calvo-Zaragoza and I. Barbancho and L. J. Tardón and A. M. Barbancho},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/318/paaa-jcalvo.pdf},
issn = {1433-7541},
year = {2015},
date = {2015-11-01},
urldate = {2015-11-01},
journal = {Pattern Analysis and Applications},
volume = {18},
number = {4},
pages = {933-943},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {article}
}
Valero-Mas, J. J.; Iñesta, J. M.
Interactive onset detection in audio recordings Technical Report
Málaga, Spain, 2015.
Abstract | Links | BibTeX | Tags: TIMuL
@techreport{k334,
title = {Interactive onset detection in audio recordings},
author = {J. J. Valero-Mas and J. M. Iñesta},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/334/OnsetInteraction-LBD.pdf},
year = {2015},
date = {2015-10-01},
booktitle = {Late Breaking/Demo extended abstract, 16th International Society for Music Information Retrieval Conference (ISMIR)},
address = {Málaga, Spain},
organization = {University of Alicante},
abstract = {Onset detection still has room for improvement. State-of-the-art onset detection algorithms achieve good results for a range of applications, but for some situations in which the accuracy is a must, human intervention is required to correct the mistakes committed. In such scheme, accuracy in the result is guaranteed at the expense of the manual correction of all errors. Hence, the issue now lies on finding schemes for efficiently exploiting and reducing that user effort. In this work we present an Interactive Pattern Recognition approach for tackling this issue: using a pre-trained classification-based onset detection algorithm, every time the user corrects an error in the estimation, the system modifies its performance accordingly and recalculates the output. Initial results show that user effort is effectively reduced under our proposal.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {techreport}
}
Onset detection still has room for improvement. State-of-the-art onset detection algorithms achieve good results for a range of applications, but for some situations in which the accuracy is a must, human intervention is required to correct the mistakes committed. In such scheme, accuracy in the result is guaranteed at the expense of the manual correction of all errors. Hence, the issue now lies on finding schemes for efficiently exploiting and reducing that user effort. In this work we present an Interactive Pattern Recognition approach for tackling this issue: using a pre-trained classification-based onset detection algorithm, every time the user corrects an error in the estimation, the system modifies its performance accordingly and recalculates the output. Initial results show that user effort is effectively reduced under our proposal.
Calvo-Zaragoza, J.; de León, P. J. Ponce; Iñesta, J. M.; Rizo, D.
Genre-based melody generation through multi-objective genetic algorithms Proceedings Article
In: Proceedings of the 8th Machine Learning and Music workshop (MML 2015), Vancouver (Canada), 2015.
Abstract | BibTeX | Tags: TIMuL
@inproceedings{k332,
title = {Genre-based melody generation through multi-objective genetic algorithms},
author = {J. Calvo-Zaragoza and P. J. Ponce de León and J. M. Iñesta and D. Rizo},
year = {2015},
date = {2015-08-01},
urldate = {2015-08-01},
booktitle = {Proceedings of the 8th Machine Learning and Music workshop (MML 2015)},
address = {Vancouver (Canada)},
abstract = {Genetic-based composition algorithms have the ability to ex- plore an immense space of possibilities but the main di},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Genetic-based composition algorithms have the ability to ex- plore an immense space of possibilities but the main di
Valero-Mas, J. J.; Salamon, J.; Gómez, E.
Analyzing the influence of pitch quantization and note segmentation on singing voice alignment in the context of audio-based Query-by-Humming Proceedings Article
In: Proceedings of the 12th Sound and Music Computing Conference (SMC), pp. 371–378, Maynooth, Ireland, 2015, ISBN: 9--7809--92746629.
Abstract | Links | BibTeX | Tags: Prometeo 2012, TIMuL
@inproceedings{k331,
title = {Analyzing the influence of pitch quantization and note segmentation on singing voice alignment in the context of audio-based Query-by-Humming},
author = {J. J. Valero-Mas and J. Salamon and E. Gómez},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/331/QBH_SMC2015_CameraReady.pdf},
isbn = {9--7809--92746629},
year = {2015},
date = {2015-07-01},
booktitle = {Proceedings of the 12th Sound and Music Computing Conference (SMC)},
pages = {371--378},
address = {Maynooth, Ireland},
abstract = {Query-by-Humming (QBH) systems base their operation on aligning the melody sung/hummed by a user with a set of candidate melodies retrieved from music tunes. While MIDI-based QBH builds on the premise of existing annotated transcriptions for any candidate song, audio-based research makes use of melody extraction algorithms for the music tunes. In both cases, a melody abstraction process is required for solving issues commonly found in queries such as key transpositions or tempo deviations. Automatic music transcription is commonly used for this, but due to the reported limitations in state-of-the-art methods for real-world queries, other possibilities should be considered. In this work we explore three different melody representations, ranging from a general time-series one to more musical abstractions, which avoid the automatic transcription step, in the context of an audio-based QBH system. Results show that this abstraction process plays a key role in the overall accuracy of the system, obtaining the best scores when temporal segmentation is dynamically performed in terms of pitch change events in the melodic contour.},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Query-by-Humming (QBH) systems base their operation on aligning the melody sung/hummed by a user with a set of candidate melodies retrieved from music tunes. While MIDI-based QBH builds on the premise of existing annotated transcriptions for any candidate song, audio-based research makes use of melody extraction algorithms for the music tunes. In both cases, a melody abstraction process is required for solving issues commonly found in queries such as key transpositions or tempo deviations. Automatic music transcription is commonly used for this, but due to the reported limitations in state-of-the-art methods for real-world queries, other possibilities should be considered. In this work we explore three different melody representations, ranging from a general time-series one to more musical abstractions, which avoid the automatic transcription step, in the context of an audio-based QBH system. Results show that this abstraction process plays a key role in the overall accuracy of the system, obtaining the best scores when temporal segmentation is dynamically performed in terms of pitch change events in the melodic contour.
Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R.
Prototype Generation on Structural Data using Dissimilarity Space Representation: A Case of Study Proceedings Article
In: Paredes, Roberto; Cardoso, Jaime S.; Pardo, Xosé M. (Ed.): 7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), pp. 72-82, Springer, Santiago de Compostela, Spain, 2015, ISBN: 978-3-319-19389-2.
Abstract | Links | BibTeX | Tags: TIMuL
@inproceedings{k325,
title = {Prototype Generation on Structural Data using Dissimilarity Space Representation: A Case of Study},
author = {J. Calvo-Zaragoza and J. J. Valero-Mas and J. R. Rico-Juan},
editor = {Roberto Paredes and Jaime S. Cardoso and Xosé M. Pardo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/325/prototype-generation-structural.pdf},
isbn = {978-3-319-19389-2},
year = {2015},
date = {2015-06-01},
booktitle = {7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)},
pages = {72-82},
publisher = {Springer},
address = {Santiago de Compostela, Spain},
abstract = {Data Reduction techniques are commonly applied in instance-based classification tasks to lower the amount of data to be processed. Prototype Selection (PS) and Prototype Generation (PG) constitute the most representative approaches. These two families differ in the way of obtaining the reduced set out of the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to better delimit decision boundaries, operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs.
This work proposes a case of study with the use of the common RandomC algorithm for mapping the initial structural data to a Dissimilarity Space (DS) representation, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that PG combined with RandomC mapping achieves a very competitive performance, although the obtained accuracy seems to be bounded by the representativity of the DS method.},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Data Reduction techniques are commonly applied in instance-based classification tasks to lower the amount of data to be processed. Prototype Selection (PS) and Prototype Generation (PG) constitute the most representative approaches. These two families differ in the way of obtaining the reduced set out of the initial one: while the former aims at selecting the most representative elements from the set, the latter creates new data out of it. Although PG is considered to better delimit decision boundaries, operations required are not so well defined in scenarios involving structural data such as strings, trees or graphs.
This work proposes a case of study with the use of the common RandomC algorithm for mapping the initial structural data to a Dissimilarity Space (DS) representation, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that PG combined with RandomC mapping achieves a very competitive performance, although the obtained accuracy seems to be bounded by the representativity of the DS method.
This work proposes a case of study with the use of the common RandomC algorithm for mapping the initial structural data to a Dissimilarity Space (DS) representation, thereby allowing the use of PG methods. A comparative experiment over string data is carried out in which our proposal is faced to PS methods on the original space. Results show that PG combined with RandomC mapping achieves a very competitive performance, although the obtained accuracy seems to be bounded by the representativity of the DS method.
Calvo-Zaragoza, J.; Oncina, J.
Clustering of Strokes from Pen-based Music Notation: An Experimental Study Proceedings Article
In: Paredes, Roberto; Cardoso, Jaime S.; Pardo, Xosé M. (Ed.): 7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), pp. 633-640, Springer, Santiago de Compostela, Spain, 2015, ISBN: 978-3-319-19389-2.
Links | BibTeX | Tags: Prometeo 2012, TIMuL
@inproceedings{k326,
title = {Clustering of Strokes from Pen-based Music Notation: An Experimental Study},
author = {J. Calvo-Zaragoza and J. Oncina},
editor = {Roberto Paredes and Jaime S. Cardoso and Xosé M. Pardo},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/326/clustering-strokes-pen.pdf},
isbn = {978-3-319-19389-2},
year = {2015},
date = {2015-06-01},
booktitle = {7th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)},
pages = {633-640},
publisher = {Springer},
address = {Santiago de Compostela, Spain},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Micó, L.; Sanches, J.; Cardoso, J. S.
The vitality of pattern recognition and image analysis Journal Article
In: Neurocomputing, vol. 150, pp. 124-125, 2015, ISSN: 09252312.
BibTeX | Tags: Prometeo 2012, TIMuL
@article{k320,
title = {The vitality of pattern recognition and image analysis},
author = {L. Micó and J. Sanches and J. S. Cardoso},
issn = {09252312},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Neurocomputing},
volume = {150},
pages = {124-125},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {article}
}
Rizo, D.; Iñesta, J. M.
A grammar for Plaine and Easie Code Proceedings Article
In: Roland, Perry; Kepper, Johannes (Ed.): Proceedings of the Music Encoding Initiative Conferences 2013 and 2014, pp. 54–64, 2015.
BibTeX | Tags: GRE-12-34, Prometeo 2012, TIMuL
@inproceedings{k321,
title = {A grammar for Plaine and Easie Code},
author = {D. Rizo and J. M. Iñesta},
editor = {Perry Roland and Johannes Kepper},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Proceedings of the Music Encoding Initiative Conferences 2013 and 2014},
pages = {54--64},
keywords = {GRE-12-34, Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Rico-Juan, J. R.; Calvo-Zaragoza, J.
Improving classification using a Confidence Matrix based on weak classifiers applied to OCR Journal Article
In: Neurocomputing, vol. 151, pp. 1354–1361, 2015, ISSN: 0925-2312.
Links | BibTeX | Tags: Prometeo 2012, TIMuL
@article{k323,
title = {Improving classification using a Confidence Matrix based on weak classifiers applied to OCR},
author = {J. R. Rico-Juan and J. Calvo-Zaragoza},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/323/cm.pdf},
issn = {0925-2312},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Neurocomputing},
volume = {151},
pages = {1354–1361},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {article}
}
Calvo-Zaragoza, J.; Valero-Mas, J. J.; Rico-Juan, J. R
Improving kNN multi-label classification in Prototype Selection scenarios using class proposals Journal Article
In: Pattern Recognition, vol. 48, no. 5, pp. 1608-1622, 2015.
Links | BibTeX | Tags: Prometeo 2012, TIMuL
@article{k324,
title = {Improving kNN multi-label classification in Prototype Selection scenarios using class proposals},
author = {J. Calvo-Zaragoza and J. J. Valero-Mas and J. R Rico-Juan},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/324/improving-knn-multi.pdf},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
journal = {Pattern Recognition},
volume = {48},
number = {5},
pages = {1608-1622},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {article}
}
2014
Illescas, P. R.; Rizo, D.; Iñesta, J. M.
Melodic analysis of polyphonic music using an interactive pattern recognition tool Proceedings Article
In: Proc. of 7th Machine Learning and Music (MML2014), Barcelona, 2014.
@inproceedings{k328,
title = {Melodic analysis of polyphonic music using an interactive pattern recognition tool},
author = {P. R. Illescas and D. Rizo and J. M. Iñesta},
year = {2014},
date = {2014-12-01},
booktitle = {Proc. of 7th Machine Learning and Music (MML2014)},
address = {Barcelona},
keywords = {TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Micó, L.; Oncina, J.
Dynamic Insertions in TLAESA fast NN Search Algorithm Proceedings Article
In: Proceedings of the 22nd International Conference on Pattern Recognition, ICPR, Stockholm, Sweden, 2014, ISBN: 978-1-4799-5208-3.
Abstract | Links | BibTeX | Tags: Prometeo 2012, TIMuL
@inproceedings{k319,
title = {Dynamic Insertions in TLAESA fast NN Search Algorithm},
author = {L. Micó and J. Oncina},
url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/319/icpr-2014.pdf},
isbn = {978-1-4799-5208-3},
year = {2014},
date = {2014-01-01},
booktitle = {Proceedings of the 22nd International Conference on Pattern Recognition, ICPR},
address = {Stockholm, Sweden},
abstract = {Nearest Neighbour search (NNS) is a widely used
technique in Pattern Recognition. In order to speed up the search many indexing techniques have been proposed. The need to work with large dynamic databases in interactive or online systems, has resulted in an increase interest in adapting or
creating fast methods to update these indexes. TLAESA is a fast search algorithm that computes a very low number of distance computations with sublinear overhead using a branch and bound technique.
In this paper, we propose a new fast updating method for the
TLAESA index. The behaviour of this index has been analysed
theoretical and experimentally. We have obtained a log-square
upper bound of the rebuilding expected time. This bound has
been verified experimentally on several synthetic and real data
experiments.},
keywords = {Prometeo 2012, TIMuL},
pubstate = {published},
tppubtype = {inproceedings}
}
Nearest Neighbour search (NNS) is a widely used
technique in Pattern Recognition. In order to speed up the search many indexing techniques have been proposed. The need to work with large dynamic databases in interactive or online systems, has resulted in an increase interest in adapting or
creating fast methods to update these indexes. TLAESA is a fast search algorithm that computes a very low number of distance computations with sublinear overhead using a branch and bound technique.
In this paper, we propose a new fast updating method for the
TLAESA index. The behaviour of this index has been analysed
theoretical and experimentally. We have obtained a log-square
upper bound of the rebuilding expected time. This bound has
been verified experimentally on several synthetic and real data
experiments.
technique in Pattern Recognition. In order to speed up the search many indexing techniques have been proposed. The need to work with large dynamic databases in interactive or online systems, has resulted in an increase interest in adapting or
creating fast methods to update these indexes. TLAESA is a fast search algorithm that computes a very low number of distance computations with sublinear overhead using a branch and bound technique.
In this paper, we propose a new fast updating method for the
TLAESA index. The behaviour of this index has been analysed
theoretical and experimentally. We have obtained a log-square
upper bound of the rebuilding expected time. This bound has
been verified experimentally on several synthetic and real data
experiments.