End-to-end optical music recognition for pianoform sheet music
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Título: | End-to-end optical music recognition for pianoform sheet music |
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Autor/es: | Ríos-Vila, Antonio | Rizo, David | Iñesta, José M. | Calvo-Zaragoza, Jorge |
Grupo/s de investigación o GITE: | Reconocimiento de Formas e Inteligencia Artificial |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Palabras clave: | Optical music recognition | Polyphonic music scores | GrandStaff | Neural networks |
Fecha de publicación: | 12-may-2023 |
Editor: | Springer Nature |
Cita bibliográfica: | International Journal on Document Analysis and Recognition (IJDAR). 2023, 26: 347-362. https://doi.org/10.1007/s10032-023-00432-z |
Resumen: | 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. |
Patrocinador/es: | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This paper is part of the MultiScore project (PID2020-118447RA-I00), funded by MCIN/AEI/10.13039/501100011033. The first author is supported by Grant ACIF/2021/356 from the “Programa I+D+i de la Generalitat Valenciana.” |
URI: | http://hdl.handle.net/10045/134340 |
ISSN: | 1433-2833 (Print) | 1433-2825 (Online) |
DOI: | 10.1007/s10032-023-00432-z |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1007/s10032-023-00432-z |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas |
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Rios-Vila_etal_2023_IJDAR.pdf | 1,22 MB | Adobe PDF | Abrir Vista previa | |
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