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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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.; 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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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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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.; 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.

Close

• doi:10.5281/zenodo.6797870

Close

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.

Close

• doi:10.1016/j.patrec.2022.04.032

Close

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}

}

Close

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}

}

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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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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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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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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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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}

}

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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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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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2021

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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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}

}

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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}

}

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