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Improving Deep Learning Methodologies for Music Emotion Recognition

2024-10-25Conference object Open access
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dc.contributor.authorLouro, Pedro Lima
dc.contributor.authorRedinho, Hugo
dc.contributor.authorMalheiro, Ricardo
dc.contributor.authorPaiva, Rui Pedro
dc.contributor.authorPanda, Renato
dc.date.accessioned2024-11-11T12:27:20Z
dc.date.available2024-11-11T12:27:20Z
dc.date.issued2024-10-25
dc.description.abstractMusic Emotion Recognition (MER) has traditionally relied on classical machine learning techniques. Progress on these techniques has plateaued due to the demanding process of crafting new, emotionally-relevant audio features. Recently, deep learning (DL) methods have surged in popularity within MER, due to their ability of automatically learning features from the input data. Nonetheless, these methods need large, high-quality labeled datasets, a well-known hurdle in MER studies. We present a comparative study of various classical and DL techniques carried out to evaluate these approaches. Most of the presented methodologies were developed by our team, if not stated otherwise. It was found that a combination of Dense Neural Networks (DNN) and Convolutional Neural Networks (CNN) achieved an 80.20% F1-score, marking an improvement of approximately 5% over the best previous results. This indicates that future research should blend both manual feature engineering and automated feature learning to enhance results.pt_PT
dc.description.sponsorshipThis work is funded by FCT - Foundation for Science and Technology, I.P., within the scope of the projects: MERGE - DOI: 10.54499/PTDC/CCI-COM/3171/2021 financed with national funds (PIDDAC) via the Portuguese State Budget; and project CISUC - UID/CEC/00326/2020 with funds from the European Social Fund, through the Regional Operational Program Centro 2020. Renato Panda was supported by Ci2 - FCT UIDP/05567/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.26/52756
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationMusic Emotion Recognition - Next Generation
dc.titleImproving Deep Learning Methodologies for Music Emotion Recognitionpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleMusic Emotion Recognition - Next Generation
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-COM%2F3171%2F2021/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017%2F2018) - Financiamento Programático/UIDP%2F05567%2F2020/PT
oaire.citation.conferencePlaceCovilhã, Portugalpt_PT
oaire.citation.endPage2pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.title30th Portuguese Conference on Pattern Recognition (RECPAD 2024)pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStreamConcurso de avaliação no âmbito do Programa Plurianual de Financiamento de Unidades de I&D (2017/2018) - Financiamento Programático
person.familyNameLima Louro
person.familyNameRedinho
person.familyNameMalheiro
person.familyNamePinto de Carvalho e Paiva
person.familyNamePanda
person.givenNamePedro Miguel
person.givenNameHugo
person.givenNameRicardo
person.givenNameRui Pedro
person.givenNameRenato
person.identifierWT5afVUAAAAJ
person.identifier.ciencia-idC315-8AA7-2C25
person.identifier.ciencia-id4517-42EE-8B4D
person.identifier.ciencia-idB81A-CB99-A4DF
person.identifier.ciencia-idAA16-002F-5AE3
person.identifier.ciencia-id661A-31CC-8D19
person.identifier.orcid0000-0003-3201-6990
person.identifier.orcid0009-0004-1547-2251
person.identifier.orcid0000-0002-3010-2732
person.identifier.orcid0000-0003-3215-3960
person.identifier.orcid0000-0003-2539-5590
person.identifier.ridL-9369-2017
person.identifier.scopus-author-id55354413900
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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