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Exploring Deep Learning Methodologies for Music Emotion Recognition

2024Conference object Open access
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dc.contributor.authorLouro, Pedro
dc.contributor.authorRedinho, Hugo
dc.contributor.authorMalheiro, Ricardo
dc.contributor.authorPaiva, Rui Pedro
dc.contributor.authorPanda, Renato
dc.date.accessioned2024-07-06T15:04:13Z
dc.date.available2024-07-06T15:04:13Z
dc.date.issued2024
dc.description.abstractClassical machine learning techniques have dominated Music Emotion Recognition (MER). However, improvements have slowed down due to the complex and time-consuming task of handcrafting new emotionally relevant audio features. Deep Learning methods have recently gained popularity in the field because of their ability to automatically learn relevant features from spectral representations of songs, eliminating such necessity. Nonetheless, there are limitations, such as the need for large amounts of quality labeled data, a common problem in MER research. To understand the effectiveness of these techniques, a comparison study using various classical machine learning and deep learning methods was conducted. The results showed that using an ensemble of a Dense Neural Network and a Convolutional Neural Network architecture resulted in a state-of-the-art 80.20% F1-score, an improvement of around 5% considering the best baseline results, concluding that future research should take advantage of both paradigms, that is, conbining handcrafted features with feature learning.pt_PT
dc.description.versioninfo:eu-repo/semantics/acceptedVersionpt_PT
dc.identifier.urihttp://hdl.handle.net/10400.26/51226
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationMusic Emotion Recognition - Next Generation
dc.relationCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA
dc.relationSmart Cities Research Center
dc.titleExploring Deep Learning Methodologies for Music Emotion Recognitionpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleMusic Emotion Recognition - Next Generation
oaire.awardTitleCENTRE FOR INFORMATICS AND SYSTEMS OF THE UNIVERSITY OF COIMBRA
oaire.awardTitleSmart Cities Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-COM%2F3171%2F2021/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00326%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05567%2F2020/PT
oaire.citation.conferencePlacePorto, Portugalpt_PT
oaire.citation.titleProceedings of the Sound and Music Computing Conference (SMC2024)pt_PT
oaire.fundingStream3599-PPCDT
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
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.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
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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