Deep Neural Networks for Document Processing of Music Score Images
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http://hdl.handle.net/10045/75358
Títol: | Deep Neural Networks for Document Processing of Music Score Images |
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Autors: | Calvo-Zaragoza, Jorge | Castellanos, Francisco J. | Vigliensoni, Gabriel | Fujinaga, Ichiro |
Grups d'investigació o GITE: | Reconocimiento de Formas e Inteligencia Artificial |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Paraules clau: | Optical Music Recognition | Music document processing | Music score images | Medieval manuscripts | Convolutional neural networks |
Àrees de coneixement: | Lenguajes y Sistemas Informáticos |
Data de publicació: | 24-d’abril-2018 |
Editor: | MDPI |
Citació bibliogràfica: | Calvo-Zaragoza J, Castellanos FJ, Vigliensoni G, Fujinaga I. Deep Neural Networks for Document Processing of Music Score Images. Applied Sciences. 2018; 8(5):654. doi:10.3390/app8050654 |
Resum: | There is an increasing interest in the automatic digitization of medieval music documents. Despite efforts in this field, the detection of the different layers of information on these documents still poses difficulties. The use of Deep Neural Networks techniques has reported outstanding results in many areas related to computer vision. Consequently, in this paper, we study the so-called Convolutional Neural Networks (CNN) for performing the automatic document processing of music score images. This process is focused on layering the image into its constituent parts (namely, background, staff lines, music notes, and text) by training a classifier with examples of these parts. A comprehensive experimentation in terms of the configuration of the networks was carried out, which illustrates interesting results as regards to both the efficiency and effectiveness of these models. In addition, a cross-manuscript adaptation experiment was presented in which the networks are evaluated on a different manuscript from the one they were trained. The results suggest that the CNN is capable of adapting its knowledge, and so starting from a pre-trained CNN reduces (or eliminates) the need for new labeled data. |
Patrocinadors: | This work was supported by the Social Sciences and Humanities Research Council of Canada, and Universidad de Alicante through grant GRE-16-04. |
URI: | http://hdl.handle.net/10045/75358 |
ISSN: | 2076-3417 |
DOI: | 10.3390/app8050654 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/article |
Drets: | © 2018 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 (http://creativecommons.org/licenses/by/4.0/). |
Revisió científica: | si |
Versió de l'editor: | https://doi.org/10.3390/app8050654 |
Apareix a la col·lecció: | INV - GRFIA - Artículos de Revistas |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
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2018_Calvo-Zaragoza_etal_ApplSci.pdf | 4,39 MB | Adobe PDF | Obrir Vista prèvia | |
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