A new optical music recognition system based on combined neural network

Bibliographic Details
Main Author: Wen,CH
Publication Date: 2015
Other Authors: Ana Maria Rebelo, Zhang,J, Jaime Cardoso
Format: Article
Language: eng
Source: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Download full: http://repositorio.inesctec.pt/handle/123456789/6089
http://dx.doi.org/10.1016/j.patrec.2015.02.002
Summary: Optical music recognition (OMR) is an important tool to recognize a scanned page of music sheet automatically, which has been applied to preserving music scores. In this paper, we propose a new OMR system to recognize the music symbols without segmentation. We present a new classifier named combined neural network (CNN) that offers superior classification capability. We conduct tests on fifteen pages of music sheets, which are real and scanned images. The tests show that the proposed method constitutes an interesting contribution to OMR.
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spelling A new optical music recognition system based on combined neural networkOptical music recognition (OMR) is an important tool to recognize a scanned page of music sheet automatically, which has been applied to preserving music scores. In this paper, we propose a new OMR system to recognize the music symbols without segmentation. We present a new classifier named combined neural network (CNN) that offers superior classification capability. We conduct tests on fifteen pages of music sheets, which are real and scanned images. The tests show that the proposed method constitutes an interesting contribution to OMR.2018-01-14T21:01:21Z2015-01-01T00:00:00Z2015info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/6089http://dx.doi.org/10.1016/j.patrec.2015.02.002engWen,CHAna Maria RebeloZhang,JJaime Cardosoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:20:02Zoai:repositorio.inesctec.pt:123456789/6089Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:34.849950Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv A new optical music recognition system based on combined neural network
title A new optical music recognition system based on combined neural network
spellingShingle A new optical music recognition system based on combined neural network
Wen,CH
title_short A new optical music recognition system based on combined neural network
title_full A new optical music recognition system based on combined neural network
title_fullStr A new optical music recognition system based on combined neural network
title_full_unstemmed A new optical music recognition system based on combined neural network
title_sort A new optical music recognition system based on combined neural network
author Wen,CH
author_facet Wen,CH
Ana Maria Rebelo
Zhang,J
Jaime Cardoso
author_role author
author2 Ana Maria Rebelo
Zhang,J
Jaime Cardoso
author2_role author
author
author
dc.contributor.author.fl_str_mv Wen,CH
Ana Maria Rebelo
Zhang,J
Jaime Cardoso
description Optical music recognition (OMR) is an important tool to recognize a scanned page of music sheet automatically, which has been applied to preserving music scores. In this paper, we propose a new OMR system to recognize the music symbols without segmentation. We present a new classifier named combined neural network (CNN) that offers superior classification capability. We conduct tests on fifteen pages of music sheets, which are real and scanned images. The tests show that the proposed method constitutes an interesting contribution to OMR.
publishDate 2015
dc.date.none.fl_str_mv 2015-01-01T00:00:00Z
2015
2018-01-14T21:01:21Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/6089
http://dx.doi.org/10.1016/j.patrec.2015.02.002
url http://repositorio.inesctec.pt/handle/123456789/6089
http://dx.doi.org/10.1016/j.patrec.2015.02.002
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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