Music Emotion Recognition with Standard and Melodic Audio Features

Detalhes bibliográficos
Autor(a) principal: Panda, Renato
Data de Publicação: 2015
Outros Autores: Rocha, Bruno, Paiva, Rui Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/94384
https://doi.org/10.1080/08839514.2015.1016389
Resumo: We propose a novel approach to music emotion recognition by combining standard and melodic features extracted directly from audio. To this end, a new audio dataset organized similarly to the one used in MIREX mood task comparison was created. From the data, 253 standard and 98 melodic features are extracted and used with several supervised learning techniques. Results show that, generally, melodic features perform better than standard audio. The best result, 64% f-measure, with only 11 features (9 melodic and 2 standard), was obtained with ReliefF feature selection and Support Vector Machines.
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spelling Music Emotion Recognition with Standard and Melodic Audio FeaturesMusic emotion recognitionMelodyMelodic audio featuresWe propose a novel approach to music emotion recognition by combining standard and melodic features extracted directly from audio. To this end, a new audio dataset organized similarly to the one used in MIREX mood task comparison was created. From the data, 253 standard and 98 melodic features are extracted and used with several supervised learning techniques. Results show that, generally, melodic features perform better than standard audio. The best result, 64% f-measure, with only 11 features (9 melodic and 2 standard), was obtained with ReliefF feature selection and Support Vector Machines.This work was supported by the MOODetector project (PTDC/EIA-EIA/102185/2008), financed by the Fundação para Ciência e a Tecnologia (FCT) and Programa Operacional Temático Factores de Competitividade (COMPETE) – Portugal, as well as the PhD Scholarship SFRH/BD/91523/ 2012, funded by the Fundação para Ciência e a Tecnologia (FCT), Programa Operacional Potencial Humano (POPH) and Fundo Social Europeu (FSE). This work was also supported by the RECARDI project (QREN 22997), funded by the Quadro de Referência Estratégica Nacional (QREN).Taylor & Francis2015info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/94384http://hdl.handle.net/10316/94384https://doi.org/10.1080/08839514.2015.1016389eng0883-95141087-6545https://www.tandfonline.com/doi/abs/10.1080/08839514.2015.1016389Panda, RenatoRocha, BrunoPaiva, Rui Pedroinfo: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:RCAAP2021-10-19T08:10:28Zoai:estudogeral.uc.pt:10316/94384Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:13:05.275653Repositório 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 Music Emotion Recognition with Standard and Melodic Audio Features
title Music Emotion Recognition with Standard and Melodic Audio Features
spellingShingle Music Emotion Recognition with Standard and Melodic Audio Features
Panda, Renato
Music emotion recognition
Melody
Melodic audio features
title_short Music Emotion Recognition with Standard and Melodic Audio Features
title_full Music Emotion Recognition with Standard and Melodic Audio Features
title_fullStr Music Emotion Recognition with Standard and Melodic Audio Features
title_full_unstemmed Music Emotion Recognition with Standard and Melodic Audio Features
title_sort Music Emotion Recognition with Standard and Melodic Audio Features
author Panda, Renato
author_facet Panda, Renato
Rocha, Bruno
Paiva, Rui Pedro
author_role author
author2 Rocha, Bruno
Paiva, Rui Pedro
author2_role author
author
dc.contributor.author.fl_str_mv Panda, Renato
Rocha, Bruno
Paiva, Rui Pedro
dc.subject.por.fl_str_mv Music emotion recognition
Melody
Melodic audio features
topic Music emotion recognition
Melody
Melodic audio features
description We propose a novel approach to music emotion recognition by combining standard and melodic features extracted directly from audio. To this end, a new audio dataset organized similarly to the one used in MIREX mood task comparison was created. From the data, 253 standard and 98 melodic features are extracted and used with several supervised learning techniques. Results show that, generally, melodic features perform better than standard audio. The best result, 64% f-measure, with only 11 features (9 melodic and 2 standard), was obtained with ReliefF feature selection and Support Vector Machines.
publishDate 2015
dc.date.none.fl_str_mv 2015
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/10316/94384
http://hdl.handle.net/10316/94384
https://doi.org/10.1080/08839514.2015.1016389
url http://hdl.handle.net/10316/94384
https://doi.org/10.1080/08839514.2015.1016389
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 0883-9514
1087-6545
https://www.tandfonline.com/doi/abs/10.1080/08839514.2015.1016389
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.publisher.none.fl_str_mv Taylor & Francis
publisher.none.fl_str_mv Taylor & Francis
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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