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A Comparison Study of Deep Learning Methodologies for Music Emotion Recognition

2024-03-29Journal article 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.accessioned2024-04-01T10:07:42Z
dc.date.available2024-04-01T10:07:42Z
dc.date.issued2024-03-29
dc.date.updated2024-04-01T09:58:12Z
dc.description.abstractClassical machine learning techniques have dominated Music Emotion Recognition. 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, combining handcrafted features with feature learning.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/s24072201pt_PT
dc.identifier.issn1424-8220
dc.identifier.slugcv-prod-4027410
dc.identifier.urihttp://hdl.handle.net/10400.26/50425
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationPTDC/CCI-COM/3171/2021pt_PT
dc.relation10.54499/PTDC/CCI-COM/3171/2021pt_PT
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/24/7/2201pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectmusic information retrievalpt_PT
dc.subjectmusic emotion recognitionpt_PT
dc.subjectdeep learningpt_PT
dc.titleA Comparison Study of Deep Learning Methodologies for Music Emotion Recognitionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue7pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume24pt_PT
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
rcaap.cv.cienciaid661A-31CC-8D19 | Renato Eduardo Silva Panda
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication953b69db-f6a0-4a54-8618-93a912df6df6
relation.isAuthorOfPublication90f8aa11-754d-424a-a877-891f5dc386ab
relation.isAuthorOfPublicationd38dd344-0942-4fcb-b740-a4713fb170e7
relation.isAuthorOfPublication238ee6f8-61cd-49b4-9392-9e7763fd35f3
relation.isAuthorOfPublication9cd470af-3968-45cc-ad6f-3b59e00ae823
relation.isAuthorOfPublication.latestForDiscovery90f8aa11-754d-424a-a877-891f5dc386ab

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