Decoupling music notation to improve end-to-end Optical Music Recognition
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Título: | Decoupling music notation to improve end-to-end Optical Music Recognition |
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Autor/es: | Alfaro-Contreras, María | Ríos-Vila, Antonio | Valero-Mas, Jose J. | 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 | Deep Learning | Connectionist Temporal Classification | Sequence Labeling |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 26-abr-2022 |
Editor: | Elsevier |
Cita bibliográfica: | Pattern Recognition Letters. 2022, 158: 157-163. https://doi.org/10.1016/j.patrec.2022.04.032 |
Resumen: | 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. |
Patrocinador/es: | This paper is part of the project I+D+i PID2020-118447RA-I00 (MultiScore), funded by MCIN/AEI/10.13039/501100011033. The first author is supported by grant FPU19/04957 from the Spanish Ministerio de Universidades. The second author is supported by grant ACIF/2021/356 from “Programa I+D+i de la Generalitat Valenciana“. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+i de la Generalitat Valenciana”. |
URI: | http://hdl.handle.net/10045/123164 |
ISSN: | 0167-8655 (Print) | 1872-7344 (Online) |
DOI: | 10.1016/j.patrec.2022.04.032 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.1016/j.patrec.2022.04.032 |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas |
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Archivo | Descripción | Tamaño | Formato | |
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Alfaro-Contreras_etal_2022_PatternRecognLett.pdf | 1,07 MB | Adobe PDF | Abrir Vista previa | |
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