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Exploring Song Segmentation for Music Emotion Variation Detection

2024-09Conference object Open access
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dc.contributor.authorFerreira, Tomas
dc.contributor.authorRedinho, Hugo
dc.contributor.authorLouro, Pedro L.
dc.contributor.authorMalheiro, Ricardo
dc.contributor.authorPaiva, Rui Pedro
dc.contributor.authorPanda, Renato
dc.date.accessioned2024-09-16T11:02:57Z
dc.date.available2024-09-16T11:02:57Z
dc.date.issued2024-09
dc.description.abstractThis paper evaluates the impact of song segmentation on Music Emotion Variation Detection (MEVD). In particular, the All-In-One song-structure segmentation system was employed to this end and compared to a fixed 1.5-sec window approach. Acoustic features were extracted for each obtained segment/window, which were classified with SVMs. The attained results (best F1-score of 55.9%) suggest that, despite its promise, the potential of this song segmentation approach was not fully exploited, possibly due to the small employed dataset. Nevertheless, preliminary results are encouraging.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/10400.26/52074
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationSmart Cities Research Center
dc.subjectMEVDpt_PT
dc.subjectmusic emotion variation detectionpt_PT
dc.subjectsong segmentationpt_PT
dc.titleExploring Song Segmentation for Music Emotion Variation Detectionpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardTitleSmart Cities Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/Concurso de Projetos IC&DT em Todos os Domínios Científicos/PTDC%2FCCI-COM%2F3171%2F2021/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05567%2F2020/PT
oaire.citation.conferencePlaceVilnius, Lithuaniapt_PT
oaire.citation.title15th International Workshop on Machine Learning and Music (MML2024), in Conjunction with ECML/PKDD 2024pt_PT
oaire.fundingStreamConcurso de Projetos IC&DT em Todos os Domínios Científicos
oaire.fundingStream6817 - DCRRNI ID
person.familyNameFerreira
person.familyNameRedinho
person.familyNameLima Louro
person.familyNameMalheiro
person.familyNamePinto de Carvalho e Paiva
person.familyNamePanda
person.givenNameTomas
person.givenNameHugo
person.givenNamePedro Miguel
person.givenNameRicardo
person.givenNameRui Pedro
person.givenNameRenato
person.identifierWT5afVUAAAAJ
person.identifier.ciencia-id4517-42EE-8B4D
person.identifier.ciencia-idC315-8AA7-2C25
person.identifier.ciencia-idB81A-CB99-A4DF
person.identifier.ciencia-idAA16-002F-5AE3
person.identifier.ciencia-id661A-31CC-8D19
person.identifier.orcid0009-0006-5102-6915
person.identifier.orcid0009-0004-1547-2251
person.identifier.orcid0000-0003-3201-6990
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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