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

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2018

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.

BibTeX | Tags: TIMuL

@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}

}

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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}

}

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/381/3586c021.pdf

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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}

}

Close

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.

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/362/CameraReady.pdf

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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.

BibTeX | Tags: TIMuL

@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}

}

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/358/recognition-pen-based.pdf

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Calvo-Zaragoza, J.; Oncina, J.

An efficient approach for Interactive Sequential Pattern Recognition Journal Article

In: Pattern Recognition, vol. 64, pp. 295-304, 2017.

Links | BibTeX | Tags: TIMuL

@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}

}

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/359/efficient-approach-sequential[...]

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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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/339/SOCO-RankMethods-2016.pdf

Close

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.

Links | BibTeX | Tags: TIMuL

@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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/360/tenor-unified-categorization.[...]

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/361/pixel-wise-binarization.pdf

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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}

}

Close

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.

Close

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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/372/staff-line-removal.pdf

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2016

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.

BibTeX | Tags: TIMuL

@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}

}

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/341/SuitabilityPSDistributedScena[...]

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/337/prototype-generation-structur[...]

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/350/musicNoteRecogIcfhr16.pdf

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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.

Links | BibTeX | Tags: TIMuL

@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}

}

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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}

}

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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}

}

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Genetic-based composition algorithms have the ability to ex- plore an immense space of possibilities but the main di

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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}

}

Close

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.

Close

• https://grfia.dlsi.ua.es/repositori/grfia/pubs/325/prototype-generation-structur[...]

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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}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/326/clustering-strokes-pen.pdf

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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}

}

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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}

}

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2014

Calvo-Zaragoza, J.; Oncina, J.

Recognition of Pen-based Music Notation with Probabilistic Machines Proceedings Article

In: Proceedings of the 7th International Workshop on Machine Learning and Music, Barcelona, Spain, 2014.

BibTeX | Tags: TIMuL

@inproceedings{k330,

title = {Recognition of Pen-based Music Notation with Probabilistic Machines},

author = {J. Calvo-Zaragoza and J. Oncina},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the 7th International Workshop on Machine Learning and Music},

address = {Barcelona, Spain},

keywords = {TIMuL},

pubstate = {published},

tppubtype = {inproceedings}

}

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