Efficient Approaches for Notation Assembly in Optical Music Recognition
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http://hdl.handle.net/10045/130019
Título: | Efficient Approaches for Notation Assembly in Optical Music Recognition |
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Autor/es: | Penarrubia, Carlos | Garrido Muñoz, Carlos | Valero-Mas, Jose J. | 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 | Notation Assembly | Neural Networks |
Fecha de publicación: | nov-2022 |
Editor: | Workshop on Reading Music Systems |
Cita bibliográfica: | Penarrubia, Carlos, et al. “Efficient Approaches for Notation Assembly in Optical Music Recognition”. In: Calvo-Zaragoza, Jorge; Pacha, Alexander; Shatri, Elona (Eds.). Proceedings of the 4th International Workshop on Reading Music Systems, 18th November, 2022, pp. 29-32 |
Resumen: | Optical Music Recognition (OMR) is the field of research that studies how to computationally read music notation from written documents. Thanks to recent advances in computer vision and deep learning, there are successful approaches that are able to locate the notation elements out of a given image. However, the stage of notation assembly, in which these elements must be related to reconstructing the musical notation itself, has received little attention in the last years. Furthermore, given the large number of elements in a music score, this stage must be efficient enough to be useful in practice. In this work, a couple of neural approaches that perform this stage efficiently are studied. Our experiments using the MUSCIMA++ handwritten sheet music corpus show that there exists an underlying trade-off between effectiveness and efficiency, since each approach shows very good results in only one of these two aspects. We hope that this work represents a starting point for further research in this important stage of the OMR. |
Patrocinador/es: | Work produced with the support of a 2021 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation. |
URI: | http://hdl.handle.net/10045/130019 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/conferenceObject |
Derechos: | © The respective authors. Licensed under a Creative Commons Attribution 4.0 International License (CC-BY-4.0). |
Revisión científica: | si |
Aparece en las colecciones: | INV - GRFIA - Comunicaciones a Congresos, Conferencias, etc. |
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