Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset

Bibliographic Details
Main Author: Malheiro, Ricardo
Publication Date: 2016
Other Authors: Panda, Renato, Gomes, Paulo J. S., Paiva, Rui Pedro
Format: Other
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: https://hdl.handle.net/10316/95162
Summary: This research addresses the role of audio and lyrics in the music emo- tion recognition. Each dimension (e.g., audio) was separately studied, as well as in a context of bimodal analysis. We perform classification by quadrant catego- ries (4 classes). Our approach is based on several audio and lyrics state-of-the-art features, as well as novel lyric features. To evaluate our approach we create a ground-truth dataset. The main conclusions show that unlike most of the similar works, lyrics performed better than audio. This suggests the importance of the new proposed lyric features and that bimodal analysis is always better than each dimension.
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spelling Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Datasetbimodal analysismusic emotion recognitionThis research addresses the role of audio and lyrics in the music emo- tion recognition. Each dimension (e.g., audio) was separately studied, as well as in a context of bimodal analysis. We perform classification by quadrant catego- ries (4 classes). Our approach is based on several audio and lyrics state-of-the-art features, as well as novel lyric features. To evaluate our approach we create a ground-truth dataset. The main conclusions show that unlike most of the similar works, lyrics performed better than audio. This suggests the importance of the new proposed lyric features and that bimodal analysis is always better than each dimension.This work was supported by CISUC (Center for Informatics and Systems of the University of Coimbra).2016info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/otherhttps://hdl.handle.net/10316/95162https://hdl.handle.net/10316/95162engMalheiro, RicardoPanda, RenatoGomes, Paulo J. S.Paiva, Rui Pedroinfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2022-05-25T02:38:06Zoai:estudogeral.uc.pt:10316/95162Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-29T05:43:15.650024Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset
title Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset
spellingShingle Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset
Malheiro, Ricardo
bimodal analysis
music emotion recognition
title_short Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset
title_full Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset
title_fullStr Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset
title_full_unstemmed Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset
title_sort Bi-Modal Music Emotion Recognition: Novel Lyrical Features and Dataset
author Malheiro, Ricardo
author_facet Malheiro, Ricardo
Panda, Renato
Gomes, Paulo J. S.
Paiva, Rui Pedro
author_role author
author2 Panda, Renato
Gomes, Paulo J. S.
Paiva, Rui Pedro
author2_role author
author
author
dc.contributor.author.fl_str_mv Malheiro, Ricardo
Panda, Renato
Gomes, Paulo J. S.
Paiva, Rui Pedro
dc.subject.por.fl_str_mv bimodal analysis
music emotion recognition
topic bimodal analysis
music emotion recognition
description This research addresses the role of audio and lyrics in the music emo- tion recognition. Each dimension (e.g., audio) was separately studied, as well as in a context of bimodal analysis. We perform classification by quadrant catego- ries (4 classes). Our approach is based on several audio and lyrics state-of-the-art features, as well as novel lyric features. To evaluate our approach we create a ground-truth dataset. The main conclusions show that unlike most of the similar works, lyrics performed better than audio. This suggests the importance of the new proposed lyric features and that bimodal analysis is always better than each dimension.
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/other
format other
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/10316/95162
https://hdl.handle.net/10316/95162
url https://hdl.handle.net/10316/95162
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.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833602450526830592

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