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}
}
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. 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}
}
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}
}
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. 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}
}
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}
}
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.
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}
}
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}
}
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.
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}
}