Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/95172
DC FieldValueLanguage
dc.contributor.authorPanda, Renato-
dc.contributor.authorPaiva, Rui Pedro-
dc.date.accessioned2021-07-04T20:42:05Z-
dc.date.available2021-07-04T20:42:05Z-
dc.date.issued2011-05-13-
dc.identifier.isbn9781617829253-
dc.identifier.urihttp://hdl.handle.net/10316/95172-
dc.description.abstractIn this paper we propose a solution for automatic mood tracking in audio music, based on supervised learning and classification. To this end, various music clips with a duration of 25 seconds, previously annotated with arousal and valence (AV) values, were used to train several models. These models were used to predict quadrants of the Thayer’s taxonomy and AV values, of small segments from full songs, revealing the mood changes over time. The system accuracy was measured by calculating the matching ratio between predicted results and full song annotations performed by volunteers. Different combinations of audio features, frameworks and other parameters were tested, resulting in an accuracy of 56.3% and showing there is still much room for improvement.eng
dc.description.sponsorshipThis work was supported by the MOODetector project (PTDC/EIA-EIA/102185/2008), financed by the Fundação para Ciência e Tecnologia - Portugal.eng
dc.language.isoengpt
dc.publisherAudio Engineering Societypt
dc.relationinfo:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Musicpt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectMood trackingeng
dc.subjectMusic emotion recognitioneng
dc.subjectRegressioneng
dc.subjectThayereng
dc.titleUsing Support Vector Machines for Automatic Mood Tracking in Audio Musiceng
dc.typeconferenceObjecteng
degois.publication.firstPage579pt
degois.publication.lastPage586pt
degois.publication.locationLondon, UKpt
degois.publication.title130th Audio Engineering Society Convention 2011 (AES 130)pt
dc.peerreviewedyespt
dc.date.embargo2011-05-13*
uc.date.periodoEmbargo0pt
item.fulltextCom Texto completo-
item.grantfulltextopen-
item.languageiso639-1en-
crisitem.project.grantnoinfo:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Music-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0003-2539-5590-
crisitem.author.orcid0000-0003-3215-3960-
Appears in Collections:I&D CISUC - Artigos em Livros de Actas
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