Audio Features for Music Emotion Recognition: a Survey
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10316/95975 https://doi.org/10.1109/TAFFC.2020.3032373 |
Resumo: | The design of meaningful audio features is a key need to advance the state-of-the-art in Music Emotion Recognition (MER). This work presents a survey on the existing emotionally-relevant computational audio features, supported by the music psychology literature on the relations between eight musical dimensions (melody, harmony, rhythm, dynamics, tone color, expressivity, texture and form) and specific emotions. Based on this review, current gaps and needs are identified and strategies for future research on feature engineering for MER are proposed, namely ideas for computational audio features that capture elements of musical form, texture and expressivity that should be further researched. Finally, although the focus of this article is on classical feature engineering methodologies (based on handcrafted features), perspectives on deep learning-based approaches are discussed. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
spelling |
Audio Features for Music Emotion Recognition: a Surveyaffective computingmusic emotion recognitionaudio feature designmusic information retrievalThe design of meaningful audio features is a key need to advance the state-of-the-art in Music Emotion Recognition (MER). This work presents a survey on the existing emotionally-relevant computational audio features, supported by the music psychology literature on the relations between eight musical dimensions (melody, harmony, rhythm, dynamics, tone color, expressivity, texture and form) and specific emotions. Based on this review, current gaps and needs are identified and strategies for future research on feature engineering for MER are proposed, namely ideas for computational audio features that capture elements of musical form, texture and expressivity that should be further researched. Finally, although the focus of this article is on classical feature engineering methodologies (based on handcrafted features), perspectives on deep learning-based approaches are discussed.This work was supported by the MERGE project (PTDC/CCI-COM/3171/2021) financed by Fundação para Ciência e a Tecnologia (FCT) - Portugal.IEEE2020info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/95975http://hdl.handle.net/10316/95975https://doi.org/10.1109/TAFFC.2020.3032373eng1949-30452371-9850https://ieeexplore.ieee.org/document/9229494Panda, RenatoMalheiro, RicardoPaiva, 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:RCAAP2022-05-25T03:34:59Zoai:estudogeral.uc.pt:10316/95975Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:14:21.319782Repositório 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 |
Audio Features for Music Emotion Recognition: a Survey |
title |
Audio Features for Music Emotion Recognition: a Survey |
spellingShingle |
Audio Features for Music Emotion Recognition: a Survey Panda, Renato affective computing music emotion recognition audio feature design music information retrieval |
title_short |
Audio Features for Music Emotion Recognition: a Survey |
title_full |
Audio Features for Music Emotion Recognition: a Survey |
title_fullStr |
Audio Features for Music Emotion Recognition: a Survey |
title_full_unstemmed |
Audio Features for Music Emotion Recognition: a Survey |
title_sort |
Audio Features for Music Emotion Recognition: a Survey |
author |
Panda, Renato |
author_facet |
Panda, Renato Malheiro, Ricardo Paiva, Rui Pedro |
author_role |
author |
author2 |
Malheiro, Ricardo Paiva, Rui Pedro |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Panda, Renato Malheiro, Ricardo Paiva, Rui Pedro |
dc.subject.por.fl_str_mv |
affective computing music emotion recognition audio feature design music information retrieval |
topic |
affective computing music emotion recognition audio feature design music information retrieval |
description |
The design of meaningful audio features is a key need to advance the state-of-the-art in Music Emotion Recognition (MER). This work presents a survey on the existing emotionally-relevant computational audio features, supported by the music psychology literature on the relations between eight musical dimensions (melody, harmony, rhythm, dynamics, tone color, expressivity, texture and form) and specific emotions. Based on this review, current gaps and needs are identified and strategies for future research on feature engineering for MER are proposed, namely ideas for computational audio features that capture elements of musical form, texture and expressivity that should be further researched. Finally, although the focus of this article is on classical feature engineering methodologies (based on handcrafted features), perspectives on deep learning-based approaches are discussed. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020 |
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/10316/95975 http://hdl.handle.net/10316/95975 https://doi.org/10.1109/TAFFC.2020.3032373 |
url |
http://hdl.handle.net/10316/95975 https://doi.org/10.1109/TAFFC.2020.3032373 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1949-3045 2371-9850 https://ieeexplore.ieee.org/document/9229494 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
IEEE |
publisher.none.fl_str_mv |
IEEE |
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 |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799134040593268736 |