Applying Automatic Translation for Optical Music Recognition’s Encoding Step
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http://hdl.handle.net/10045/114453
Títol: | Applying Automatic Translation for Optical Music Recognition’s Encoding Step |
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Autors: | Ríos-Vila, Antonio | Esplà-Gomis, Miquel | Rizo, David | Ponce de León Amador, Pedro José | Iñesta, José M. |
Grups d'investigació o GITE: | Transducens | Reconocimiento de Formas e Inteligencia Artificial | Blockchain Aplicado a las Empresas (BAES) |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Paraules clau: | Optical music recognition | Machine translation | Machine learning | Computer vision | Intermediate representation | Humdrum |
Àrees de coneixement: | Lenguajes y Sistemas Informáticos |
Data de publicació: | 25-d’abril-2021 |
Editor: | MDPI |
Citació bibliogràfica: | Ríos-Vila A, Esplà-Gomis M, Rizo D, Ponce de León PJ, Iñesta JM. Applying Automatic Translation for Optical Music Recognition’s Encoding Step. Applied Sciences. 2021; 11(9):3890. https://doi.org/10.3390/app11093890 |
Resum: | Optical music recognition is a research field whose efforts have been mainly focused, due to the difficulties involved in its processes, on document and image recognition. However, there is a final step after the recognition phase that has not been properly addressed or discussed, and which is relevant to obtaining a standard digital score from the recognition process: the step of encoding data into a standard file format. In this paper, we address this task by proposing and evaluating the feasibility of using machine translation techniques, using statistical approaches and neural systems, to automatically convert the results of graphical encoding recognition into a standard semantic format, which can be exported as a digital score. We also discuss the implications, challenges and details to be taken into account when applying machine translation techniques to music languages, which are very different from natural human languages. This needs to be addressed prior to performing experiments and has not been reported in previous works. We also describe and detail experimental results, and conclude that applying machine translation techniques is a suitable solution for this task, as they have proven to obtain robust results. |
Patrocinadors: | This work was supported by the Spanish Ministry HISPAMUS project TIN2017-86576-R, partially funded by the EU, and by the Generalitat Valenciana through project GV/2020/030. |
URI: | http://hdl.handle.net/10045/114453 |
ISSN: | 2076-3417 |
DOI: | 10.3390/app11093890 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.3390/app11093890 |
Apareix a la col·lecció: | INV - TRANSDUCENS - Artículos de Revistas |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
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Rios-Vila_etal_2021_ApplSci.pdf | 3,88 MB | Adobe PDF | Obrir Vista prèvia | |
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