Acc. Int. E-A ARFAI DOREMI DRIMS ENJAMBRE GRE-12-34 GRE16-14 GRE19-04 GV-JRRJ GV/2020/030 HispaMus ISIC 2010 Leonardo2021 MIPRCV MultiScore PASCAL2 Prometeo 2012 PROSEMUS REPERTORIUM ROMA TADMar TIASA TIMuL UA-CPS

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. Maciá, M. 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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115 entries « ‹ 1 of 3 › »

2024

Alfaro-Contreras, M.; Rios-Vila, A.; Valero-Mas, J. J.; Calvo-Zaragoza, J.

A Transformer Approach for Polyphonic Audio-to-Score Transcription Proceedings Article

In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024), Seul (Korea), 2024.

BibTeX | Tags: MultiScore

@inproceedings{Alfaro-Contreras:ICASSP24,

title = {A Transformer Approach for Polyphonic Audio-to-Score Transcription},

author = {M. Alfaro-Contreras and A. Rios-Vila and J. J. Valero-Mas and J. Calvo-Zaragoza},

year  = {2024},

date = {2024-04-19},

urldate = {2024-04-19},

booktitle = {Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)},

address = {Seul (Korea)},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

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2023

Martínez-Sevilla, J. C.; Ríos-Vila, A.; Castellanos, F. J.; Calvo-Zaragoza, J.

A Holistic Approach for Aligned Music and Lyrics Transcription Conference

Document Analysis and Recognition - ICDAR 2023, vol. 1, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-41676-7.

Abstract | Links | BibTeX | Tags: REPERTORIUM

@conference{MartinezSevilla:ICDAR:2023,

title = {A Holistic Approach for Aligned Music and Lyrics Transcription},

author = {J.C. Martínez-Sevilla and A. Ríos-Vila and F. J. Castellanos and J. Calvo-Zaragoza },

editor = {Fink, Gernot A. and Jain, Rajiv and Kise, Koichi and Zanibbi, Richard},

doi = {https://doi.org/10.1007/978-3-031-41676-7_11},

isbn = {978-3-031-41676-7},

year  = {2023},

date = {2023-08-28},

urldate = {2023-08-28},

booktitle = {Document Analysis and Recognition - ICDAR 2023},

volume = {1},

pages = {185--201},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {In this paper, we present the Aligned Music Notation and Lyrics Transcription (AMNLT) challenge, whose goal is to retrieve the content from document images of vocal music. This new research area arises from the need to automatically transcribe notes and lyrics from music scores and align both sources of information conveniently. Although existing methods are able to deal with music notation and text, they work without providing their proper alignment, which is crucial to actually retrieve the content of the piece of vocal music. To overcome this challenge, we consider holistic neural approaches that transcribe music and text in one step, along with an encoding that implicitly aligns the sources of information. The methodology is evaluated on a benchmark specifically designed for AMNLT. The results report that existing methods can obtain high-quality text and music transcriptions, but posterior alignment errors are inevitably found. However, our formulation achieves relative improvements of over 80{%} in the metric that considers both transcription and alignment. We hope that this work will establish itself as a future reference for further research on AMNLT.},

keywords = {REPERTORIUM},

pubstate = {published},

tppubtype = {conference}

}

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In this paper, we present the Aligned Music Notation and Lyrics Transcription (AMNLT) challenge, whose goal is to retrieve the content from document images of vocal music. This new research area arises from the need to automatically transcribe notes and lyrics from music scores and align both sources of information conveniently. Although existing methods are able to deal with music notation and text, they work without providing their proper alignment, which is crucial to actually retrieve the content of the piece of vocal music. To overcome this challenge, we consider holistic neural approaches that transcribe music and text in one step, along with an encoding that implicitly aligns the sources of information. The methodology is evaluated on a benchmark specifically designed for AMNLT. The results report that existing methods can obtain high-quality text and music transcriptions, but posterior alignment errors are inevitably found. However, our formulation achieves relative improvements of over 80{%} in the metric that considers both transcription and alignment. We hope that this work will establish itself as a future reference for further research on AMNLT.

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• doi:https://doi.org/10.1007/978-3-031-41676-7_11

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Martínez-Sevilla, J. C.; Alfaro-Contreras, M.; Valero-Mas, J. J.; Calvo-Zaragoza, J.

Insights into end-to-end audio-to-score transcription with real recordings: A case study with saxophone works Proceedings Article

In: INTERSPEECH Conference, pp. 2793-2797, Dublin, Ireland, 2023.

Links | BibTeX | Tags: MultiScore

@inproceedings{Martínez-Sevilla2023,

title = {Insights into end-to-end audio-to-score transcription with real recordings: A case study with saxophone works},

author = {J.C. Martínez-Sevilla and M. Alfaro-Contreras and J. J. Valero-Mas and J. Calvo-Zaragoza



},

doi = {10.21437/Interspeech.2023-88},

year  = {2023},

date = {2023-08-20},

urldate = {2023-08-20},

booktitle = {INTERSPEECH Conference},

pages = {2793-2797},

address = {Dublin, Ireland},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

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• doi:10.21437/Interspeech.2023-88

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Alfaro-Contreras, M.; Valero-Mas, J. J.; Iñesta, J. M.; Calvo-Zaragoza, J.

Multimodal Strategies for Image and Audio Music Transcription: A Comparative Study Proceedings Article

In: Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, pp. 64-77, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-37731-0.

Links | BibTeX | Tags: MultiScore

@inproceedings{k505,

title = {Multimodal Strategies for Image and Audio Music Transcription: A Comparative Study},

author = {M. Alfaro-Contreras and J. J. Valero-Mas and J. M. Iñesta and J. Calvo-Zaragoza},

doi = {10.1007/978-3-031-37731-0_6},

isbn = {978-3-031-37731-0},

year  = {2023},

date = {2023-08-10},

urldate = {2022-01-01},

booktitle = {Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science},

volume = {13645},

pages = {64-77},

publisher = {Springer Nature Switzerland},

address = {Cham},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

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• doi:10.1007/978-3-031-37731-0_6

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Garrido-Munoz, C.; Alfaro-Contreras, M.; Calvo-Zaragoza, J.

Evaluating Domain Generalization in Kitchen Utensils Classification Proceedings Article

In: Iberian Conference on Pattern Recognition and Image Analysis, pp. 108-118, 2023.

Links | BibTeX | Tags: MultiScore

@inproceedings{Garrido-Munoz2023,

title = {Evaluating Domain Generalization in Kitchen Utensils Classification},

author = {C. Garrido-Munoz and M. Alfaro-Contreras and J. Calvo-Zaragoza},

doi = {10.1007/978-3-031-36616-1_9},

year  = {2023},

date = {2023-06-25},

booktitle = {Iberian Conference on Pattern Recognition and Image Analysis},

pages = {108-118},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

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• doi:10.1007/978-3-031-36616-1_9

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González-Barrachina, P.; Alfaro-Contreras, M.; Nieto-Hidalgo, M.; Calvo-Zaragoza, J.

Lifelong Learning for Document Image Binarization: An Experimental Study Proceedings Article

In: Iberian Conference on Pattern Recognition and Image Analysis, pp. 146-157, 2023.

Links | BibTeX | Tags: MultiScore

@inproceedings{González-Barrachina2023,

title = {Lifelong Learning for Document Image Binarization: An Experimental Study},

author = {P. González-Barrachina and M. Alfaro-Contreras and M. Nieto-Hidalgo and J. Calvo-Zaragoza },

doi = {10.1007/978-3-031-36616-1_12},

year  = {2023},

date = {2023-06-25},

urldate = {2023-06-25},

booktitle = {Iberian Conference on Pattern Recognition and Image Analysis},

pages = {146-157},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

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• doi:10.1007/978-3-031-36616-1_12

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Alfaro-Contreras, M.; Iñesta, J. M.; Calvo-Zaragoza, J.

Optical Music Recognition for Homophonic Scores with Neural Networks and Synthetic Music Generation Journal Article

In: International Journal of Multimedia Information Retrieval, vol. 12, pp. 12-24, 2023.

Links | BibTeX | Tags:

@article{Alfaro-Contreras2023b,

title = {Optical Music Recognition for Homophonic Scores with Neural Networks and Synthetic Music Generation},

author = {M. Alfaro-Contreras and J. M. Iñesta and J. Calvo-Zaragoza},

doi = {10.1007/s13735-023-00278-5},

year  = {2023},

date = {2023-05-26},

urldate = {2023-05-26},

journal = {International Journal of Multimedia Information Retrieval},

volume = {12},

pages = {12-24},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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• doi:10.1007/s13735-023-00278-5

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Ríos-Vila, A.; Rizo, D.; Iñesta, J. M.; Calvo-Zaragoza, J.

End-to-end optical music recognition for pianoform sheet music Journal Article

In: International Journal on Document Analysis and Recognition (IJDAR), iss. ICDAR 2023, 2023, ISSN: 1433-2825.

Abstract | Links | BibTeX | Tags: MultiScore

@article{Ríos-Vila2023,

title = {End-to-end optical music recognition for pianoform sheet music},

author = {A. Ríos-Vila and D. Rizo and J. M. Iñesta and J. Calvo-Zaragoza},

url = {https://link.springer.com/content/pdf/10.1007/s10032-023-00432-z.pdf},

doi = {10.1007/s10032-023-00432-z},

issn = {1433-2825},

year  = {2023},

date = {2023-05-12},

urldate = {2023-05-12},

journal = {International Journal on Document Analysis and Recognition (IJDAR)},

issue = {ICDAR 2023},

abstract = {End-to-end solutions have brought about significant advances in the field of Optical Music Recognition. These approaches directly provide the symbolic representation of a given image of a musical score. Despite this, several documents, such as pianoform musical scores, cannot yet benefit from these solutions since their structural complexity does not allow their effective transcription. This paper presents a neural method whose objective is to transcribe these musical scores in an end-to-end fashion. We also introduce the GrandStaff dataset, which contains 53,882 single-system piano scores in common western modern notation. The sources are encoded in both a standard digital music representation and its adaptation for current transcription technologies. The method proposed in this paper is trained and evaluated using this dataset. The results show that the approach presented is, for the first time, able to effectively transcribe pianoform notation in an end-to-end manner.},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {article}

}

Close

End-to-end solutions have brought about significant advances in the field of Optical Music Recognition. These approaches directly provide the symbolic representation of a given image of a musical score. Despite this, several documents, such as pianoform musical scores, cannot yet benefit from these solutions since their structural complexity does not allow their effective transcription. This paper presents a neural method whose objective is to transcribe these musical scores in an end-to-end fashion. We also introduce the GrandStaff dataset, which contains 53,882 single-system piano scores in common western modern notation. The sources are encoded in both a standard digital music representation and its adaptation for current transcription technologies. The method proposed in this paper is trained and evaluated using this dataset. The results show that the approach presented is, for the first time, able to effectively transcribe pianoform notation in an end-to-end manner.

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• https://link.springer.com/content/pdf/10.1007/s10032-023-00432-z.pdf
• doi:10.1007/s10032-023-00432-z

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Alfaro-Contreras, M.; Ríos-Vila, A.; Valero-Mas, J. J.; Calvo-Zaragoza, J.

Few-Shot Symbol Classification via Self-Supervised Learning and Nearest Neighbor Journal Article

In: Pattern Recognition Letters, vol. 167, pp. 1-8, 2023.

Links | BibTeX | Tags: MultiScore

@article{Alfaro-Contreras2023,

title = {Few-Shot Symbol Classification via Self-Supervised Learning and Nearest Neighbor},

author = {M. Alfaro-Contreras and A. Ríos-Vila and J. J. Valero-Mas and J. Calvo-Zaragoza},

doi = {10.1016/j.patrec.2023.01.014},

year  = {2023},

date = {2023-03-01},

journal = {Pattern Recognition Letters},

volume = {167},

pages = {1-8},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {article}

}

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• doi:10.1016/j.patrec.2023.01.014

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Sánchez-Ferrer, A.; Valero-Mas, J. J.; Gallego, A. J.; Calvo-Zaragoza, J.

An Experimental Study on Marine Debris Location and Recognition using Object Detection Journal Article

In: Pattern Recognition Letters, 2023, ISSN: 0167-8655.

Abstract | BibTeX | Tags: TADMar

@article{k521,

title = {An Experimental Study on Marine Debris Location and Recognition using Object Detection},

author = {A. Sánchez-Ferrer and J. J. Valero-Mas and A. J. Gallego and J. Calvo-Zaragoza},

issn = {0167-8655},

year  = {2023},

date = {2023-01-01},

journal = {Pattern Recognition Letters},

abstract = {The large amount of debris in our oceans is a global problem that dramatically impacts marine fauna and flora. While a large number of human-based campaigns have been proposed to tackle this issue, these efforts have been deemed insufficient due to the insurmountable amount of existing litter. In response to that, there exists a high interest in the use of autonomous underwater vehicles (AUV) that may locate, identify, and collect this garbage automatically. To perform such a task, AUVs consider state-of-the-art object detection techniques based on deep neural networks due to their reported high performance. Nevertheless, these techniques generally require large amounts of data with fine-grained annotations. In this work, we explore the capabilities of the reference object detector Mask Region-based Convolutional Neural Networks for automatic marine debris location and classification in the context of limited data availability. Considering the recent CleanSea corpus, we pose several scenarios regarding the amount of available train data and study the possibility of mitigating the adverse effects of data scarcity with synthetic marine scenes. Our results achieve a new state of the art in the task, establishing a new reference for future research. In addition, it is shown that the task still has room for improvement and that the lack of data can be somehow alleviated, yet to a limited extent.},

keywords = {TADMar},

pubstate = {published},

tppubtype = {article}

}

Close

The large amount of debris in our oceans is a global problem that dramatically impacts marine fauna and flora. While a large number of human-based campaigns have been proposed to tackle this issue, these efforts have been deemed insufficient due to the insurmountable amount of existing litter. In response to that, there exists a high interest in the use of autonomous underwater vehicles (AUV) that may locate, identify, and collect this garbage automatically. To perform such a task, AUVs consider state-of-the-art object detection techniques based on deep neural networks due to their reported high performance. Nevertheless, these techniques generally require large amounts of data with fine-grained annotations. In this work, we explore the capabilities of the reference object detector Mask Region-based Convolutional Neural Networks for automatic marine debris location and classification in the context of limited data availability. Considering the recent CleanSea corpus, we pose several scenarios regarding the amount of available train data and study the possibility of mitigating the adverse effects of data scarcity with synthetic marine scenes. Our results achieve a new state of the art in the task, establishing a new reference for future research. In addition, it is shown that the task still has room for improvement and that the lack of data can be somehow alleviated, yet to a limited extent.

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Alfaro-Contreras, M.; Valero-Mas, J. J.; Iñesta, J. M.; Calvo-Zaragoza, J.

Late multimodal fusion for image and audio music transcription Journal Article

In: Expert Systems With Applications, vol. 216, pp. 119491-119500, 2023.

Links | BibTeX | Tags: MultiScore

@article{Alfaro-Contreras2023c,

title = {Late multimodal fusion for image and audio music transcription},

author = {M. Alfaro-Contreras and J. J. Valero-Mas and J. M. Iñesta and J. Calvo-Zaragoza},

doi = {10.1016/j.eswa.2022.119491},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Expert Systems With Applications},

volume = {216},

pages = {119491-119500},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {article}

}

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• doi:10.1016/j.eswa.2022.119491

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2022

Ríos-Vila, A.; Iñesta, J. M.; Calvo-Zaragoza, J.

End-to-End Full-Page Optical Music Recognition for Mensural Notation Proceedings Article

In: Proceedings of the 23rd International Society for Music Information Retrieval Conference, pp. 226-232, 2022, ISBN: 978-1-7327299-2-6.

Abstract | Links | BibTeX | Tags: Leonardo2021, MultiScore

@inproceedings{Ríos-Vila2022,

title = {End-to-End Full-Page Optical Music Recognition for Mensural Notation},

author = {A. Ríos-Vila and J. M. Iñesta and J. Calvo-Zaragoza},

url = {https://zenodo.org/record/7342678/files/000026.pdf?download=1},

doi = {https://doi.org/10.5281/zenodo.7342678},

isbn = {978-1-7327299-2-6},

year  = {2022},

date = {2022-12-04},

urldate = {2022-12-04},

booktitle = {Proceedings of the 23rd International Society for Music Information Retrieval Conference},

journal = {Proceedings of the 23nd International Society for Music Information Retrieval Conference},

pages = {226-232},

abstract = {Optical Music Recognition (OMR) systems typically consider workflows that include several steps, such as staff detection, symbol recognition, and semantic reconstruction. However, fine-tuning these systems is costly due to the specific data labeling process that has to be performed to train models for each of these steps. In this paper, we present the first segmentation-free full-page OMR system that receives a page image and directly outputs the transcription in a single step. This model requires only the annotations of full score pages, which greatly alleviates the task of manual labeling. The model has been tested with early music written in mensural notation, for which the presented approach is especially beneficial. Results show that this methodology provides a solution with promising results and establishes a new line of research for holistic transcription of music score pages.},

keywords = {Leonardo2021, MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Optical Music Recognition (OMR) systems typically consider workflows that include several steps, such as staff detection, symbol recognition, and semantic reconstruction. However, fine-tuning these systems is costly due to the specific data labeling process that has to be performed to train models for each of these steps. In this paper, we present the first segmentation-free full-page OMR system that receives a page image and directly outputs the transcription in a single step. This model requires only the annotations of full score pages, which greatly alleviates the task of manual labeling. The model has been tested with early music written in mensural notation, for which the presented approach is especially beneficial. Results show that this methodology provides a solution with promising results and establishes a new line of research for holistic transcription of music score pages.

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• https://zenodo.org/record/7342678/files/000026.pdf?download=1
• doi:https://doi.org/10.5281/zenodo.7342678

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Rizo, D.; Delgado, T.; Calvo-Zaragoza, J.; Madueño, A.; García-Iasci, P.

Speeding-up the encoding of mensural collections from Spanish libraries Journal Article

In: IAML 2022 Prague, 2022.

BibTeX | Tags: MultiScore

@article{k502,

title = {Speeding-up the encoding of mensural collections from Spanish libraries},

author = {D. Rizo and T. Delgado and J. Calvo-Zaragoza and A. Madueño and P. García-Iasci},

year  = {2022},

date = {2022-07-01},

booktitle = {IAML 2022 Prague},

journal = {IAML 2022 Prague},

organization = {IAML},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {article}

}

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Alfaro-Contreras, M.; Ríos-Vila, A.; Valero-Mas, J. J.; Iñesta, J. M.; Calvo-Zaragoza, J.

Decoupling music notation to improve end-to-end Optical Music Recognition Journal Article

In: Pattern Recognition Letters, vol. 158, pp. 157-163, 2022, ISSN: 0167-8655.

Abstract | Links | BibTeX | Tags: MultiScore

@article{Alfaro-Contreras2022,

title = {Decoupling music notation to improve end-to-end Optical Music Recognition},

author = {M. Alfaro-Contreras and A. Ríos-Vila and J. J. Valero-Mas and J. M. Iñesta and J. Calvo-Zaragoza},

doi = {10.1016/j.patrec.2022.04.032},

issn = {0167-8655},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

journal = {Pattern Recognition Letters},

volume = {158},

pages = {157-163},

abstract = {Inspired by the Text Recognition field, end-to-end schemes based on Convolutional Recurrent Neural Networks (CRNN) trained with the Connectionist Temporal Classification (CTC) loss function are considered one of the current state-of-the-art techniques for staff-level Optical Music Recognition (OMR). Unlike text symbols, music-notation elements may be defined as a combination of (i) a shape primitive located in (ii) a certain position in a staff. However, this double nature is generally neglected in the learning process, as each combination is treated as a single token. In this work, we study whether exploiting such particularity of music notation actually benefits the recognition performance and, if so, which approach is the most appropriate. For that, we thoroughly review existing specific approaches that explore this premise and propose different combinations of them. Furthermore, considering the limitations observed in such approaches, a novel decoding strategy specifically designed for OMR is proposed. The results obtained with four different corpora of historical manuscripts show the relevance of leveraging this double nature of music notation since it outperforms the standard approaches where it is ignored. In addition, the proposed decoding leads to significant reductions in the error rates with respect to the other cases.},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {article}

}

Close

Inspired by the Text Recognition field, end-to-end schemes based on Convolutional Recurrent Neural Networks (CRNN) trained with the Connectionist Temporal Classification (CTC) loss function are considered one of the current state-of-the-art techniques for staff-level Optical Music Recognition (OMR). Unlike text symbols, music-notation elements may be defined as a combination of (i) a shape primitive located in (ii) a certain position in a staff. However, this double nature is generally neglected in the learning process, as each combination is treated as a single token. In this work, we study whether exploiting such particularity of music notation actually benefits the recognition performance and, if so, which approach is the most appropriate. For that, we thoroughly review existing specific approaches that explore this premise and propose different combinations of them. Furthermore, considering the limitations observed in such approaches, a novel decoding strategy specifically designed for OMR is proposed. The results obtained with four different corpora of historical manuscripts show the relevance of leveraging this double nature of music notation since it outperforms the standard approaches where it is ignored. In addition, the proposed decoding leads to significant reductions in the error rates with respect to the other cases.

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• doi:10.1016/j.patrec.2022.04.032

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Alfaro-Contreras, M.; Valero-Mas, J. J.; Iñesta, J. M.; Calvo-Zaragoza, J.

Insights into transfer learning between image and audio music transcription Proceedings Article

In: Sound and Music Computing Conference, pp. 295-301, Zenodo, Saint-Étienne, France, 2022.

Abstract | Links | BibTeX | Tags: MultiScore

@inproceedings{Alfaro-Contreras2022b,

title = {Insights into transfer learning between image and audio music transcription},

author = {M. Alfaro-Contreras and J. J. Valero-Mas and J. M. Iñesta and J. Calvo-Zaragoza},

doi = {10.5281/zenodo.6797870},

year  = {2022},

date = {2022-06-01},

urldate = {2022-06-01},

booktitle = {Sound and Music Computing Conference},

pages = {295-301},

publisher = {Zenodo},

address = {Saint-Étienne, France},

abstract = {Optical Music Recognition (OMR) and Automatic Music Transcription (AMT) stand for the research fields that devise methods to transcribe music sources---documents or audio signals, respectively---into a structured digital format. Historically, they have followed different approaches to achieve the same goal. However, their recent definition in terms of sequence labeling tasks gathers them under a common formulation framework. Under this premise, one may wonder if there exist any synergies between the two fields that could be exploited to improve the individual recognition rates in their respective domains. In this work, we aim to further explore this question from a Transfer Learning (TL) point of view in the context of neural end-to-end recognition models. More precisely, we consider a music transcription system, trained on either image or audio data, and adapt its performance to the unseen domain during the training phase using different TL schemes. Results show that knowledge transfer slightly boosts model performance with sufficient available data, but it is not properly leveraged when the latter condition is not met. This opens up a new promising, yet challenging, research path towards building an effective bridge between two solutions of the same problem.},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Optical Music Recognition (OMR) and Automatic Music Transcription (AMT) stand for the research fields that devise methods to transcribe music sources---documents or audio signals, respectively---into a structured digital format. Historically, they have followed different approaches to achieve the same goal. However, their recent definition in terms of sequence labeling tasks gathers them under a common formulation framework. Under this premise, one may wonder if there exist any synergies between the two fields that could be exploited to improve the individual recognition rates in their respective domains. In this work, we aim to further explore this question from a Transfer Learning (TL) point of view in the context of neural end-to-end recognition models. More precisely, we consider a music transcription system, trained on either image or audio data, and adapt its performance to the unseen domain during the training phase using different TL schemes. Results show that knowledge transfer slightly boosts model performance with sufficient available data, but it is not properly leveraged when the latter condition is not met. This opens up a new promising, yet challenging, research path towards building an effective bridge between two solutions of the same problem.

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• doi:10.5281/zenodo.6797870

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Arroyo, V.; Valero-Mas, J. J.; Calvo-Zaragoza, J.; Pertusa, A.

Neural audio-to-score music transcription for unconstrained polyphony using compact output representations Proceedings Article

In: Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Singapur, Singapur, 2022.

BibTeX | Tags: MultiScore

@inproceedings{k487,

title = {Neural audio-to-score music transcription for unconstrained polyphony using compact output representations},

author = {V. Arroyo and J. J. Valero-Mas and J. Calvo-Zaragoza and A. Pertusa},

year  = {2022},

date = {2022-05-01},

booktitle = {Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

publisher = {IEEE},

address = {Singapur, Singapur},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

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Garrido-Munoz, C.; Ríos-Vila, A.; Calvo-Zaragoza, J.

Retrieval of Music-Notation Primitives via Image-to-Sequence Approaches Proceedings Article

In: Iberian Pattern Recognition and Image Analysis, IbPRIA 2022., pp. 482-492, Aveiro, Portugal, 2022, ISBN: 978-3-031-04880-7.

BibTeX | Tags: Leonardo2021

@inproceedings{k493,

title = {Retrieval of Music-Notation Primitives via Image-to-Sequence Approaches},

author = {C. Garrido-Munoz and A. Ríos-Vila and J. Calvo-Zaragoza},

isbn = {978-3-031-04880-7},

year  = {2022},

date = {2022-05-01},

booktitle = {Iberian Pattern Recognition and Image Analysis, IbPRIA 2022.},

pages = {482-492},

address = {Aveiro, Portugal},

keywords = {Leonardo2021},

pubstate = {published},

tppubtype = {inproceedings}

}

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Mas-Candela, E.; Ríos-Vila, A.; Calvo-Zaragoza, J.

A First Approach to Image Transformation Sequence Retrieval Proceedings Article

In: Iberian Pattern Recognition and Image Analysis, IbPRIA 2022., pp. 321-332, Aveiro, Portugal, 2022.

BibTeX | Tags: MultiScore

@inproceedings{k494,

title = {A First Approach to Image Transformation Sequence Retrieval},

author = {E. Mas-Candela and A. Ríos-Vila and J. Calvo-Zaragoza},

year  = {2022},

date = {2022-05-01},

booktitle = {Iberian Pattern Recognition and Image Analysis, IbPRIA 2022.},

pages = {321-332},

address = {Aveiro, Portugal},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

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Sánchez-Ferrer, A.; Gallego, A. J.; Valero-Mas, J. J.; Calvo-Zaragoza, J.

The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and Recognition Proceedings Article

In: 10th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), pp. 616–628, Aveiro, Portugal, 2022, ISBN: 978-3-031-04881-4.

Abstract | BibTeX | Tags:

@inproceedings{k512,

title = {The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and Recognition},

author = {A. Sánchez-Ferrer and A. J. Gallego and J. J. Valero-Mas and J. Calvo-Zaragoza},

isbn = {978-3-031-04881-4},

year  = {2022},

date = {2022-05-01},

booktitle = {10th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)},

pages = {616--628},

address = {Aveiro, Portugal},

abstract = {In recent years, the large amount of debris scattered throughout the ocean is becoming one of the major pollution problems, causing extinction of species and accelerating the degradation of our planet, among other environmental issues. Since the manual treatment of this waste represents a considerably tedious task, autonomous frameworks are gaining attention. Due to their reported good performance, such frameworks generally rely on Deep Learning techniques. However, the scarcity of data coupled with the inherent difficulties of the field---debris with different shapes and colors due to long-lasting exposure to the ocean, illumination variability or sea conditions---makes detecting underwater objects a particularly challenging task. The contribution of this work to the field is double: on the one hand, we introduce a novel data collection for supervised learning---the CleanSea corpus---annotated at both the bound box and contour levels of the objects to contribute with the research and progress in the field and on the other hand, we devise and optimize a recognition model based on the reference Mask Object-Based Convolutional Neural Network for this set to establish a benchmark for future comparison and assess its performance in both simulated and real-world scenarios. Results show the relevance of the contributions as the devised model is capable of properly addressing the detection and recognition of general debris when trained with the introduced CleanSea corpus.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

In recent years, the large amount of debris scattered throughout the ocean is becoming one of the major pollution problems, causing extinction of species and accelerating the degradation of our planet, among other environmental issues. Since the manual treatment of this waste represents a considerably tedious task, autonomous frameworks are gaining attention. Due to their reported good performance, such frameworks generally rely on Deep Learning techniques. However, the scarcity of data coupled with the inherent difficulties of the field---debris with different shapes and colors due to long-lasting exposure to the ocean, illumination variability or sea conditions---makes detecting underwater objects a particularly challenging task. The contribution of this work to the field is double: on the one hand, we introduce a novel data collection for supervised learning---the CleanSea corpus---annotated at both the bound box and contour levels of the objects to contribute with the research and progress in the field and on the other hand, we devise and optimize a recognition model based on the reference Mask Object-Based Convolutional Neural Network for this set to establish a benchmark for future comparison and assess its performance in both simulated and real-world scenarios. Results show the relevance of the contributions as the devised model is capable of properly addressing the detection and recognition of general debris when trained with the introduced CleanSea corpus.

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Ríos-Vila, A.; Iñesta, J. M.; Calvo-Zaragoza, J.

On the Use of Transformers for End-to-End Optical Music Recognition Proceedings Article

In: Iberian Pattern Recognition and Image Analysis, IbPRIA 2022., pp. 470-481, Aveiro, Portugal, 2022, ISBN: 978-3-031-04880-7.

BibTeX | Tags: MultiScore

@inproceedings{k492,

title = {On the Use of Transformers for End-to-End Optical Music Recognition},

author = {A. Ríos-Vila and J. M. Iñesta and J. Calvo-Zaragoza},

isbn = {978-3-031-04880-7},

year  = {2022},

date = {2022-05-01},

urldate = {2022-05-01},

booktitle = {Iberian Pattern Recognition and Image Analysis, IbPRIA 2022.},

pages = {470-481},

address = {Aveiro, Portugal},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Fuente, C.; Valero-Mas, J. J.; Castellanos, F. J.; Calvo-Zaragoza, J.

Multimodal Image and Audio Music Transcription Journal Article

In: International Journal of Multimedia Information Retrieval, vol. 11, pp. 77-84, 2022.

BibTeX | Tags: MultiScore

@article{k479,

title = {Multimodal Image and Audio Music Transcription},

author = {C. Fuente and J. J. Valero-Mas and F. J. Castellanos and J. Calvo-Zaragoza},

year  = {2022},

date = {2022-01-01},

journal = {International Journal of Multimedia Information Retrieval},

volume = {11},

pages = {77-84},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {article}

}

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Rosello, A.; Ayllon, E.; Valero-Mas, J. J.; Calvo-Zaragoza, J.

Test Sample Selection for Handwriting Recognition Through Language Modeling Proceedings Article

In: Pattern Recognition and Image Analysis - 10th Iberian Conference, IbPRIA 2022, Aveiro, Portugal, May 4-6, 2022, Proceedings, 2022.

BibTeX | Tags: MultiScore

@inproceedings{k498,

title = {Test Sample Selection for Handwriting Recognition Through Language Modeling},

author = {A. Rosello and E. Ayllon and J. J. Valero-Mas and J. Calvo-Zaragoza},

year  = {2022},

date = {2022-01-01},

booktitle = {Pattern Recognition and Image Analysis - 10th Iberian Conference, IbPRIA 2022, Aveiro, Portugal, May 4-6, 2022, Proceedings},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

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Castellanos, F. J.; Gallego, A. J.; Calvo-Zaragoza, J.; Fujinaga, I.

Domain Adaptation for Staff-Region Retrieval of Music Score Images Journal Article

In: International Journal on Document Analysis and Recognition, 2022, ISSN: 1433-2825.

BibTeX | Tags: MultiScore

@article{k500,

title = {Domain Adaptation for Staff-Region Retrieval of Music Score Images},

author = {F. J. Castellanos and A. J. Gallego and J. Calvo-Zaragoza and I. Fujinaga},

issn = {1433-2825},

year  = {2022},

date = {2022-01-01},

journal = {International Journal on Document Analysis and Recognition},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {article}

}

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Alfaro-Contreras, M.; Ríos-Vila, A.; Valero-Mas, J. J.; Calvo-Zaragoza, J.

Few-Shot Music Symbol Classification via Self-Supervised Learning and Nearest Neighbor Proceedings Article

In: Pattern Recognition. ICPR International Workshops and Challenges, 2022.

BibTeX | Tags: MultiScore

@inproceedings{k504,

title = {Few-Shot Music Symbol Classification via Self-Supervised Learning and Nearest Neighbor},

author = {M. Alfaro-Contreras and A. Ríos-Vila and J. J. Valero-Mas and J. Calvo-Zaragoza},

year  = {2022},

date = {2022-01-01},

booktitle = {Pattern Recognition. ICPR International Workshops and Challenges},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

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Sáez-Pérez, J.; Gallego, A. J.; Valero-Mas, J. J.; Calvo-Zaragoza, J.

Domain Adaptation in Robotics: A Study Case on Kitchen Utensil Recognition Proceedings Article

In: 10th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), 2022.

BibTeX | Tags: ROMA

@inproceedings{k506,

title = {Domain Adaptation in Robotics: A Study Case on Kitchen Utensil Recognition},

author = {J. Sáez-Pérez and A. J. Gallego and J. J. Valero-Mas and J. Calvo-Zaragoza},

year  = {2022},

date = {2022-01-01},

booktitle = {10th Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA)},

keywords = {ROMA},

pubstate = {published},

tppubtype = {inproceedings}

}

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Garrido-Munoz, C.; Ríos-Vila, A.; Calvo-Zaragoza, J.

A holistic approach for image-to-graph: application to optical music recognition Journal Article

In: International Journal on Document Analysis and Recognition, 2022.

BibTeX | Tags: Leonardo2021

@article{k522,

title = {A holistic approach for image-to-graph: application to optical music recognition},

author = {C. Garrido-Munoz and A. Ríos-Vila and J. Calvo-Zaragoza},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {International Journal on Document Analysis and Recognition},

keywords = {Leonardo2021},

pubstate = {published},

tppubtype = {article}

}

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Ríos-Vila, A.; Iñesta, J. M.; Calvo-Zaragoza, J.

End-to-End Full-Page Optical Music Recognition for Mensural Notation Proceedings Article

In: Proceedings of the 23rd International Society for Music Information Retrieval Conference, ISMIR, Bangalore, India, 2022.

Abstract | BibTeX | Tags: Leonardo2021, MultiScore

@inproceedings{k499,

title = {End-to-End Full-Page Optical Music Recognition for Mensural Notation},

author = {A. Ríos-Vila and J. M. Iñesta and J. Calvo-Zaragoza},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {Proceedings of the 23rd International Society for Music Information Retrieval Conference, ISMIR},

address = {Bangalore, India},

abstract = {Optical Music Recognition (OMR) systems typically consider workflows that include several steps, such as staff detection, symbol recognition, and semantic reconstruction. However, fine-tuning these systems is costly due to the specific data labeling process that has to be performed to train models for each of these steps. In this paper, we present the first segmentation-free full-page OMR system that receives a page image and directly outputs the transcription in a single step. This model requires only the annotations of full score pages, which greatly alleviates the task of manual labeling. The model has been tested with early music written in mensural notation, for which the presented approach is especially beneficial. Results show that this methodology provides a solution with promising results and establishes a new line of research for holistic transcription of music score pages.},

keywords = {Leonardo2021, MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Optical Music Recognition (OMR) systems typically consider workflows that include several steps, such as staff detection, symbol recognition, and semantic reconstruction. However, fine-tuning these systems is costly due to the specific data labeling process that has to be performed to train models for each of these steps. In this paper, we present the first segmentation-free full-page OMR system that receives a page image and directly outputs the transcription in a single step. This model requires only the annotations of full score pages, which greatly alleviates the task of manual labeling. The model has been tested with early music written in mensural notation, for which the presented approach is especially beneficial. Results show that this methodology provides a solution with promising results and establishes a new line of research for holistic transcription of music score pages.

Close

Castellanos, F. J.; Garrido-Munoz, C.; Ríos-Vila, A.; Calvo-Zaragoza, J.

Region-based Layout Analysis of Music Score Images Journal Article

In: Expert Systems with Applications, pp. 118211, 2022, ISSN: 0957-4174.

BibTeX | Tags: MultiScore

@article{k486,

title = {Region-based Layout Analysis of Music Score Images},

author = {F. J. Castellanos and C. Garrido-Munoz and A. Ríos-Vila and J. Calvo-Zaragoza},

issn = {0957-4174},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Expert Systems with Applications},

pages = {118211},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {article}

}

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de la Fuente, C.; Castellanos, F. J.; Valero-Mas, J. J.; Calvo-Zaragoza, J.

Multimodal Recognition of Frustration during Game-Play with Deep Neural Networks Journal Article

In: Multimedia Tools and Applications, 2022.

BibTeX | Tags:

@article{k501,

title = {Multimodal Recognition of Frustration during Game-Play with Deep Neural Networks},

author = {C. de la Fuente and F. J. Castellanos and J. J. Valero-Mas and J. Calvo-Zaragoza},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Multimedia Tools and Applications},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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2021

Castellanos, F. J.; Gallego, A. J.; Calvo-Zaragoza, J.

An Unsupervised Domain Adaptation framework for Layout Analysis of Music Score Images Proceedings Article

In: Proceedings of the 14th Machine Learning and Music Workshop, pp. 6, 2021.

BibTeX | Tags: GRE19-04, ROMA

@inproceedings{k482,

title = {An Unsupervised Domain Adaptation framework for Layout Analysis of Music Score Images},

author = {F. J. Castellanos and A. J. Gallego and J. Calvo-Zaragoza},

year  = {2021},

date = {2021-12-01},

booktitle = {Proceedings of the 14th Machine Learning and Music Workshop},

pages = {6},

keywords = {GRE19-04, ROMA},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Ríos-Vila, A.; Calvo-Zaragoza, J.; Iñesta, J. M.

CTC-based end-to-end approach for full page Optical Music Recognition Proceedings Article

In: Proceedings of the 14th Machine Learning and Music Workshop, pp. 11, 2021.

BibTeX | Tags: MultiScore

@inproceedings{k488,

title = {CTC-based end-to-end approach for full page Optical Music Recognition},

author = {A. Ríos-Vila and J. Calvo-Zaragoza and J. M. Iñesta},

year  = {2021},

date = {2021-12-01},

booktitle = {Proceedings of the 14th Machine Learning and Music Workshop},

pages = {11},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Calvo-Zaragoza, J.; Pertusa, A.; Gallego, A. J.; Iñesta, J. M.; Micó, L.; Oncina, J.; Perez-Sancho, C.; de León, P. J. Ponce; Rizo, D.

MultiScore Project: Multimodal Transcription of Music Scores Proceedings Article

In: Proceedings of the 14th Machine Learning and Music Workshop, pp. 3, 2021.

Links | BibTeX | Tags: MultiScore

@inproceedings{k481,

title = {MultiScore Project: Multimodal Transcription of Music Scores},

author = {J. Calvo-Zaragoza and A. Pertusa and A. J. Gallego and J. M. Iñesta and L. Micó and J. Oncina and C. Perez-Sancho and P. J. Ponce de León and D. Rizo},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/481/MML2021__MultiScore_Final.pdf},

year  = {2021},

date = {2021-12-01},

urldate = {2021-12-01},

booktitle = {Proceedings of the 14th Machine Learning and Music Workshop},

pages = {3},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/481/MML2021__MultiScore_Final.pdf

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Castellanos, F. J.; Gallego, A. J.; Calvo-Zaragoza, J.

Unsupervised Neural Domain Adaptation for Document Image Binarization Journal Article

In: Pattern Recognition, vol. 119, pp. 108099, 2021.

BibTeX | Tags: GRE19-04, HispaMus

@article{k467,

title = {Unsupervised Neural Domain Adaptation for Document Image Binarization},

author = {F. J. Castellanos and A. J. Gallego and J. Calvo-Zaragoza},

year  = {2021},

date = {2021-11-01},

urldate = {2021-11-01},

journal = {Pattern Recognition},

volume = {119},

pages = {108099},

keywords = {GRE19-04, HispaMus},

pubstate = {published},

tppubtype = {article}

}

Close

Alfaro-Contreras, M.; Rizo, D.; Iñesta, J. M.; Calvo-Zaragoza, J.

OMR-assisted transcription: a case study with early prints Proceedings Article

In: Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR, pp. 35-41, 2021, ISBN: 978-1-7327299-0-2.

BibTeX | Tags: MultiScore

@inproceedings{k483,

title = {OMR-assisted transcription: a case study with early prints},

author = {M. Alfaro-Contreras and D. Rizo and J. M. Iñesta and J. Calvo-Zaragoza},

isbn = {978-1-7327299-0-2},

year  = {2021},

date = {2021-11-01},

urldate = {2021-11-01},

booktitle = {Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR},

pages = {35-41},

keywords = {MultiScore},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Fuente, C; Valero-Mas, J. J.; Castellanos, F. J.; Calvo-Zaragoza, J.

Multimodal Audio and Image Music Transcription Proceedings Article

In: Proc. of the 3rd International Workshop on Reading Music Systems, pp. 18-22, 2021.

BibTeX | Tags:

@inproceedings{k469,

title = {Multimodal Audio and Image Music Transcription},

author = {C Fuente and J. J. Valero-Mas and F. J. Castellanos and J. Calvo-Zaragoza},

year  = {2021},

date = {2021-01-01},

booktitle = {Proc. of the 3rd International Workshop on Reading Music Systems},

pages = {18-22},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

Close

Calvo-Zaragoza, J.; Rizo, D.; Iñesta, J. M.

Reconocimiento Óptico de Partituras (OMR) aplicado al Fonde de Música Tradicional IMF-CSIC Book Chapter

In: Gambero-Ustárroz, M.; Ros-Fábregas, E. (Ed.): Musicología en Web. Patrimonio musical y Humanidades Digitales, Chapter 4, pp. 87-109, Edition Reichenberger, 2021, ISBN: 978-3-967280-14-2.

BibTeX | Tags: HispaMus

@inbook{k470,

title = {Reconocimiento Óptico de Partituras (OMR) aplicado al Fonde de Música Tradicional IMF-CSIC},

author = {J. Calvo-Zaragoza and D. Rizo and J. M. Iñesta},

editor = {M. Gambero-Ustárroz and E. Ros-Fábregas},

isbn = {978-3-967280-14-2},

year  = {2021},

date = {2021-01-01},

booktitle = {Musicología en Web. Patrimonio musical y Humanidades Digitales},

pages = {87-109},

publisher = {Edition Reichenberger},

chapter = {4},

keywords = {HispaMus},

pubstate = {published},

tppubtype = {inbook}

}

Close

Garrido-Munoz, C.; Sánchez-Hernández, A.; Castellanos, F. J.; Calvo-Zaragoza, J.

Domain Adaptation for Document Image Binarization via Domain Classification Proceedings Article

In: Tallón-Ballesteros, A. J. (Ed.): Frontiers in Artificial Intelligence and Applications, pp. 569-582, IOS Press, 2021, ISBN: 978-1-64368-224-2.

BibTeX | Tags: GRE19-04, GV/2020/030

@inproceedings{k480,

title = {Domain Adaptation for Document Image Binarization via Domain Classification},

author = {C. Garrido-Munoz and A. Sánchez-Hernández and F. J. Castellanos and J. Calvo-Zaragoza},

editor = {A. J. Tallón-Ballesteros},

isbn = {978-1-64368-224-2},

year  = {2021},

date = {2021-01-01},

booktitle = {Frontiers in Artificial Intelligence and Applications},

pages = {569-582},

publisher = {IOS Press},

chapter = {-},

keywords = {GRE19-04, GV/2020/030},

pubstate = {published},

tppubtype = {inproceedings}

}

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Gallego, A. J.; Calvo-Zaragoza, J.; Fisher, R. B.

Incremental Unsupervised Domain-Adversarial Training of Neural Networks Journal Article

In: IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 11, pp. 4864-4878, 2021, ISSN: 2162-2388.

Abstract | Links | BibTeX | Tags: GRE19-04, HispaMus

@article{k455,

title = {Incremental Unsupervised Domain-Adversarial Training of Neural Networks},

author = {A. J. Gallego and J. Calvo-Zaragoza and R. B. Fisher},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/455/2001.04129.pdf},

issn = {2162-2388},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {IEEE Transactions on Neural Networks and Learning Systems},

volume = {32},

number = {11},

pages = {4864-4878},

abstract = {In the context of supervised statistical learning, it is typically assumed that the training set comes from the same distribution that draws the test samples. When this is not the case, the behavior of the learned model is unpredictable and becomes dependent upon the degree of similarity between the distribution of the training set and the distribution of the test set. One of the research topics that investigates this scenario is referred to as Domain Adaptation (DA). Deep neural networks brought dramatic advances in pattern recognition and that is why there have been many attempts to provide good domain adaptation algorithms for these models. Here we take a different avenue and approach the problem from an incremental point of view, where the model is adapted to the new domain iteratively. We make use of an existing unsupervised domain-adaptation algorithm to identify the target samples on which there is greater confidence about their true label. The output of the model is analyzed in different ways to determine the candidate samples. The selected samples are then added to the source training set by self-labeling, and the process is repeated until all target samples are labeled. This approach implements a form of adversarial training in which, by moving the self-labeled samples from the target to the source set, the DA algorithm is forced to look for new features after each iteration. Our results report a clear improvement with respect to the non-incremental case in several datasets, also outperforming other state-of-the-art domain adaptation algorithms.},

keywords = {GRE19-04, HispaMus},

pubstate = {published},

tppubtype = {article}

}

Close

In the context of supervised statistical learning, it is typically assumed that the training set comes from the same distribution that draws the test samples. When this is not the case, the behavior of the learned model is unpredictable and becomes dependent upon the degree of similarity between the distribution of the training set and the distribution of the test set. One of the research topics that investigates this scenario is referred to as Domain Adaptation (DA). Deep neural networks brought dramatic advances in pattern recognition and that is why there have been many attempts to provide good domain adaptation algorithms for these models. Here we take a different avenue and approach the problem from an incremental point of view, where the model is adapted to the new domain iteratively. We make use of an existing unsupervised domain-adaptation algorithm to identify the target samples on which there is greater confidence about their true label. The output of the model is analyzed in different ways to determine the candidate samples. The selected samples are then added to the source training set by self-labeling, and the process is repeated until all target samples are labeled. This approach implements a form of adversarial training in which, by moving the self-labeled samples from the target to the source set, the DA algorithm is forced to look for new features after each iteration. Our results report a clear improvement with respect to the non-incremental case in several datasets, also outperforming other state-of-the-art domain adaptation algorithms.

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/455/2001.04129.pdf

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Ríos-Vila, A.; Rizo, D.; Calvo-Zaragoza, J.

Complete Optical Music Recognition via Agnostic Transcription and Machine Translation Proceedings Article

In: 16th International Conference on Document Analysis and Recognition, pp. 661-675, 2021.

BibTeX | Tags: GV/2020/030

@inproceedings{k477,

title = {Complete Optical Music Recognition via Agnostic Transcription and Machine Translation},

author = {A. Ríos-Vila and D. Rizo and J. Calvo-Zaragoza},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {16th International Conference on Document Analysis and Recognition},

pages = {661-675},

keywords = {GV/2020/030},

pubstate = {published},

tppubtype = {inproceedings}

}

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Castellanos, F. J.; Valero-Mas, J. J.; Calvo-Zaragoza, J.

Prototype Generation in the String Space via Approximate Median for Data Reduction in Nearest Neighbor classification Journal Article

In: Soft Computing, vol. 25, 2021, ISSN: 15403-15415.

BibTeX | Tags: GRE19-04, HispaMus

@article{k476,

title = {Prototype Generation in the String Space via Approximate Median for Data Reduction in Nearest Neighbor classification},

author = {F. J. Castellanos and J. J. Valero-Mas and J. Calvo-Zaragoza},

issn = {15403-15415},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

journal = {Soft Computing},

volume = {25},

keywords = {GRE19-04, HispaMus},

pubstate = {published},

tppubtype = {article}

}

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Castellanos, F. J.; Gallego, A. J.; Calvo-Zaragoza, J.

Unsupervised Domain Adaptation for Document Analysis of Music Score Images Proceedings Article

In: Proc. of the 22nd International Society for Music Information Retrieval Conference, 2021.

BibTeX | Tags: GRE19-04

@inproceedings{k475,

title = {Unsupervised Domain Adaptation for Document Analysis of Music Score Images},

author = {F. J. Castellanos and A. J. Gallego and J. Calvo-Zaragoza},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Proc. of the 22nd International Society for Music Information Retrieval Conference},

keywords = {GRE19-04},

pubstate = {published},

tppubtype = {inproceedings}

}

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Román, M. A.

An End-to-End Framework for Audio-to-Score Music Transcription PhD Thesis

2021.

BibTeX | Tags: HispaMus

@phdthesis{k462,

title = {An End-to-End Framework for Audio-to-Score Music Transcription},

author = {M. A. Román},

editor = {J. Calvo-Zaragoza and A. Pertusa},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

organization = {Universidad de Alicante},

keywords = {HispaMus},

pubstate = {published},

tppubtype = {phdthesis}

}

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Mas-Candela, E.; Alfaro-Contreras, M.; Calvo-Zaragoza, J.

Sequential Next-Symbol Prediction for Optical Music Recognition Proceedings Article

In: 16th International Conference on Document Analysis and Recognition, pp. 708-722, 2021, ISBN: 978-3-030-86334-0.

Links | BibTeX | Tags: GV/2020/030

@inproceedings{k478,

title = {Sequential Next-Symbol Prediction for Optical Music Recognition},

author = {E. Mas-Candela and M. Alfaro-Contreras and J. Calvo-Zaragoza},

url = {https://link.springer.com/chapter/10.1007/978-3-030-86334-0_46},

isbn = {978-3-030-86334-0},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {16th International Conference on Document Analysis and Recognition},

pages = {708-722},

keywords = {GV/2020/030},

pubstate = {published},

tppubtype = {inproceedings}

}

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• https://link.springer.com/chapter/10.1007/978-3-030-86334-0_46

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López-Gutiérrez, J. C.; Valero-Mas, J. J.; Castellanos, F. J.; Calvo-Zaragoza, J.

Data Augmentation for End-to-End Optical Music Recognition Proceedings Article

In: Proceedings of the 14th IAPR International Workshop on Graphics Recognition (GREC), pp. 59-73, Springer, 2021.

BibTeX | Tags: GV/2020/030

@inproceedings{k473,

title = {Data Augmentation for End-to-End Optical Music Recognition},

author = {J. C. López-Gutiérrez and J. J. Valero-Mas and F. J. Castellanos and J. Calvo-Zaragoza},

year  = {2021},

date = {2021-01-01},

urldate = {2021-01-01},

booktitle = {Proceedings of the 14th IAPR International Workshop on Graphics Recognition (GREC)},

pages = {59-73},

publisher = {Springer},

keywords = {GV/2020/030},

pubstate = {published},

tppubtype = {inproceedings}

}

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2020

Ríos-Vila, A.; Calvo-Zaragoza, J.; Rizo, D.

Evaluating Simultaneous Recognition and Encoding for Optical Music Recognition Proceedings Article

In: DLfM 2020: 7th International Conference on Digital Libraries for Musicology, pp. 10-17, Association for Computing Machinery, 2020, ISBN: 978-1-4503-8760-6.

BibTeX | Tags: HispaMus

@inproceedings{k456,

title = {Evaluating Simultaneous Recognition and Encoding for Optical Music Recognition},

author = {A. Ríos-Vila and J. Calvo-Zaragoza and D. Rizo},

isbn = {978-1-4503-8760-6},

year  = {2020},

date = {2020-10-01},

booktitle = {DLfM 2020: 7th International Conference on Digital Libraries for Musicology},

journal = {DLfM 2020: 7th International Conference on Digital Libraries for Musicology},

pages = {10-17},

publisher = {Association for Computing Machinery},

keywords = {HispaMus},

pubstate = {published},

tppubtype = {inproceedings}

}

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Castellanos, F. J.; Calvo-Zaragoza, J.; Iñesta, J. M.

A neural approach for full-page Optical Music Recognition of mensural documents Proceedings Article

In: Proceedings of the 21st International Society for Music Information Retrieval Conference, pp. 558-565, Montréal, Canada, 2020, ISBN: 978-0-9813537-0-8.

BibTeX | Tags: HispaMus

@inproceedings{k457,

title = {A neural approach for full-page Optical Music Recognition of mensural documents},

author = {F. J. Castellanos and J. Calvo-Zaragoza and J. M. Iñesta},

isbn = {978-0-9813537-0-8},

year  = {2020},

date = {2020-10-01},

booktitle = {Proceedings of the 21st International Society for Music Information Retrieval Conference},

pages = {558-565},

address = {Montréal, Canada},

keywords = {HispaMus},

pubstate = {published},

tppubtype = {inproceedings}

}

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Castellanos, F. J.; Calvo-Zaragoza, J.; Iñesta, J. M.

Two-step neural cross-domain experiments forfull-page recognition of Mensural documents Proceedings Article

In: Proc. of the 13th International Workshop on Machine Learning and Music, pp. 56–60, MML 2020 13th International Workshop on Machine Learning and Music, 2020.

BibTeX | Tags: GRE19-04, HispaMus

@inproceedings{k448,

title = {Two-step neural cross-domain experiments forfull-page recognition of Mensural documents},

author = {F. J. Castellanos and J. Calvo-Zaragoza and J. M. Iñesta},

year  = {2020},

date = {2020-09-01},

booktitle = {Proc. of the 13th International Workshop on Machine Learning and Music},

pages = {56--60},

publisher = {13th International Workshop on Machine Learning and Music},

organization = {MML 2020},

keywords = {GRE19-04, HispaMus},

pubstate = {published},

tppubtype = {inproceedings}

}

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Castellanos, F. J.; Gallego, A. J.; Calvo-Zaragoza, J.

Automatic scale estimation for music score images Journal Article

In: Expert Systems with Applications, vol. 158, no. 113590, 2020, ISSN: 0957-4174.

BibTeX | Tags:

@article{k450,

title = {Automatic scale estimation for music score images},

author = {F. J. Castellanos and A. J. Gallego and J. Calvo-Zaragoza},

issn = {0957-4174},

year  = {2020},

date = {2020-06-01},

urldate = {2020-06-01},

journal = {Expert Systems with Applications},

volume = {158},

number = {113590},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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Thomae, M. E.; Ríos-Vila, A.; Calvo-Zaragoza, J.; Rizo, D.; Iñesta, J. M.

Retrieving Music Semantics from Optical Music Recognition by Machine Translation Proceedings Article

In: Luca, Elsa De; Flanders, Julia (Ed.): Music Encoding Conference Proceedings 26-29 May, 2020 Tufts University, Boston (USA), pp. 19-24, Music Encoding Initiative 2020.

Links | BibTeX | Tags: HispaMus

@inproceedings{k446,

title = {Retrieving Music Semantics from Optical Music Recognition by Machine Translation},

author = {M. E. Thomae and A. Ríos-Vila and J. Calvo-Zaragoza and D. Rizo and J. M. Iñesta},

editor = {Elsa De Luca and Julia Flanders},

url = {https://grfia.dlsi.ua.es/repositori/grfia/pubs/446/music_encoding_conference_proceedings_2020.pdf},

year  = {2020},

date = {2020-05-01},

booktitle = {Music Encoding Conference Proceedings 26-29 May, 2020 Tufts University, Boston (USA)},

pages = {19-24},

organization = {Music Encoding Initiative},

keywords = {HispaMus},

pubstate = {published},

tppubtype = {inproceedings}

}

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• https://grfia.dlsi.ua.es/repositori/grfia/pubs/446/music_encoding_conference_pro[...]

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Calvo-Zaragoza, J.; Jr., J. Hajic; Pacha, A.

Understanding Optical Music Recognition Journal Article

In: ACM Computing Surveys, vol. 53, no. 4, pp. 77, 2020.

BibTeX | Tags: HispaMus

@article{k426,

title = {Understanding Optical Music Recognition},

author = {J. Calvo-Zaragoza and J. Hajic Jr. and A. Pacha},

year  = {2020},

date = {2020-01-01},

journal = {ACM Computing Surveys},

volume = {53},

number = {4},

pages = {77},

keywords = {HispaMus},

pubstate = {published},

tppubtype = {article}

}

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