A new optical music recognition system based on combined neural network

Detalhes bibliográficos
Autor(a) principal: Wen, Cuihong
Data de Publicação: 2015
Outros Autores: Rebelo, Ana, Zhang, Jing, Cardoso, Jaime S.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/11328/2501
Resumo: 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.
id RCAP_5c00ab182a7ff866cdec9d9d24106579
oai_identifier_str oai:repositorio.uportu.pt:11328/2501
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str
spelling A new optical music recognition system based on combined neural networkNeural networkOptical music recognitionImage processingOptical 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.Elsevier2019-01-02T17:17:29Z2015-01-01T00:00:00Z2015info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/11328/2501eng0167-865510.1016/j.patrec.2015.02.002Wen, CuihongRebelo, AnaZhang, JingCardoso, Jaime S.info: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:RCAAP2023-06-15T02:10:50ZPortal AgregadorONG
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, Cuihong
Neural network
Optical music recognition
Image processing
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, Cuihong
author_facet Wen, Cuihong
Rebelo, Ana
Zhang, Jing
Cardoso, Jaime S.
author_role author
author2 Rebelo, Ana
Zhang, Jing
Cardoso, Jaime S.
author2_role author
author
author
dc.contributor.author.fl_str_mv Wen, Cuihong
Rebelo, Ana
Zhang, Jing
Cardoso, Jaime S.
dc.subject.por.fl_str_mv Neural network
Optical music recognition
Image processing
topic Neural network
Optical music recognition
Image processing
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
2019-01-02T17:17:29Z
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://hdl.handle.net/11328/2501
url http://hdl.handle.net/11328/2501
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0167-8655
10.1016/j.patrec.2015.02.002
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.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1777302553028984832

Registros relacionados