Exploiting the Two-Dimensional Nature of Agnostic Music Notation for Neural Optical Music Recognition
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Título: | Exploiting the Two-Dimensional Nature of Agnostic Music Notation for Neural Optical Music Recognition |
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Autor/es: | Alfaro-Contreras, María | Valero-Mas, Jose J. |
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 | Deep learning | Connectionist temporal classification | Agnostic music notation | Sequence labeling |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos |
Fecha de publicación: | 17-abr-2021 |
Editor: | MDPI |
Cita bibliográfica: | Alfaro-Contreras M, Valero-Mas JJ. Exploiting the Two-Dimensional Nature of Agnostic Music Notation for Neural Optical Music Recognition. Applied Sciences. 2021; 11(8):3621. https://doi.org/10.3390/app11083621 |
Resumen: | State-of-the-art Optical Music Recognition (OMR) techniques follow an end-to-end or holistic approach, i.e., a sole stage for completely processing a single-staff section image and for retrieving the symbols that appear therein. Such recognition systems are characterized by not requiring an exact alignment between each staff and their corresponding labels, hence facilitating the creation and retrieval of labeled corpora. Most commonly, these approaches consider an agnostic music representation, which characterizes music symbols by their shape and height (vertical position in the staff). However, this double nature is ignored since, in the learning process, these two features are treated as a single symbol. This work aims to exploit this trademark that differentiates music notation from other similar domains, such as text, by introducing a novel end-to-end approach to solve the OMR task at a staff-line level. We consider two Convolutional Recurrent Neural Network (CRNN) schemes trained to simultaneously extract the shape and height information and to propose different policies for eventually merging them at the actual neural level. The results obtained for two corpora of monophonic early music manuscripts prove that our proposal significantly decreases the recognition error in figures ranging between 14.4% and 25.6% in the best-case scenarios when compared to the baseline considered. |
Patrocinador/es: | This research work was partially funded by the University of Alicante through project GRE19-04, by the “Programa I+D+i de la Generalitat Valenciana” through grant APOSTD/2020/256, and by the Spanish Ministerio de Universidades through grant FPU19/04957. |
URI: | http://hdl.handle.net/10045/114284 |
ISSN: | 2076-3417 |
DOI: | 10.3390/app11083621 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 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/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/app11083621 |
Aparece en las colecciones: | INV - GRFIA - Artículos de Revistas |
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