Region-based layout analysis of music score images
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Título: | Region-based layout analysis of music score images |
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Autor/es: | Castellanos, Francisco J. | Garrido Muñoz, Carlos | Ríos-Vila, Antonio | 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 |
Palabras clave: | Optical Music Recognition | Layout Analysis | Image augmentation | Object detection |
Fecha de publicación: | 21-jul-2022 |
Editor: | Elsevier |
Cita bibliográfica: | Expert Systems with Applications. 2022, 209: 118211. https://doi.org/10.1016/j.eswa.2022.118211 |
Resumen: | The Layout Analysis (LA) stage is of vital importance to the correct performance of an Optical Music Recognition (OMR) system. It identifies the regions of interest, such as staves or lyrics, which must then be processed in order to transcribe their content. Despite the existence of modern approaches based on deep learning, an exhaustive study of LA in OMR has not yet been carried out with regard to the performance of different models, their generalization to different domains or, more importantly, their impact on subsequent stages of the pipeline. This work focuses on filling this gap in the literature by means of an experimental study of different neural architectures, music document types, and evaluation scenarios. The need for training data has also led to a proposal for a new semi-synthetic data-generation technique that enables the efficient applicability of LA approaches in real scenarios. Our results show that: (i) the choice of the model and its performance are crucial for the entire transcription process; (ii) the metrics commonly used to evaluate the LA stage do not always correlate with the final performance of the OMR system, and (iii) the proposed data-generation technique enables state-of-the-art results to be achieved with a limited set of labeled data. |
Patrocinador/es: | This paper is part of the I+D+i PID2020-118447RA-I00 (MultiScore) project funded by MCIN/AEI/10.13039/501100011033, Spain and the GV/2020/030, Spain project funded by the Generalitat Valenciana, Spain. The first and third authors acknowledge support from the “Programa I+D+i de la Generalitat Valenciana, Spain ” through grants ACIF/2019/042 and ACIF/2021/356, respectively. |
URI: | http://hdl.handle.net/10045/126121 |
ISSN: | 0957-4174 (Print) | 1873-6793 (Online) |
DOI: | 10.1016/j.eswa.2022.118211 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2022 The Author(s). Published by Elsevier Ltd. 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.eswa.2022.118211 |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
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Castellanos_etal_2022_ESWA.pdf | 2,5 MB | Adobe PDF | Abrir Vista previa | |
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