Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/95170
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dc.contributor.authorPanda, Renato-
dc.contributor.authorPaiva, Rui Pedro-
dc.date.accessioned2021-07-04T20:21:58Z-
dc.date.available2021-07-04T20:21:58Z-
dc.date.issued2011-07-06-
dc.identifier.isbn978-88-97385-03-5-
dc.identifier.issn2518-3672-
dc.identifier.urihttp://hdl.handle.net/10316/95170-
dc.description.abstractWe propose an approach for the automatic creation of mood playlists in the Thayer plane (TP). Music emotion recognition is tackled as a regression and classification problem, aiming to predict the arousal and valence (AV) values of each song in the TP, based on Yang's dataset. To this end, a high number of audio features are extracted using three frameworks: PsySound, MIR Toolbox and Marsyas. The extracted features and Yang's annotated AV values are used to train several Support Vector Regressors, each employing different feature sets. The best performance, in terms of R2statistics, was attained after feature selection, reaching 63% for arousal and 35.6% for valence. Based on the predicted location of each song in the TP, mood playlists can be created by specifying a point in the plane, from which the closest songs are retrieved. Using one seed song, the accuracy of the created playlists was 62.3% for 20-song playlists, 24.8% for 5-song playlists and 6.2% for the top song.pt
dc.description.sponsorshipThis work was supported by the MOODetector project (PTDC/EIA-EIA/102185/2008), financed by the Funda- ção para Ciência e Tecnologia - Portugal.pt
dc.language.isoengpt
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.subjectclassificationpt
dc.subjectmood detectionpt
dc.subjectmusic emotion recognitionpt
dc.subjectplaylist generationpt
dc.subjectregressionpt
dc.titleAutomatic Creation of Mood Playlists in the Thayer Plane: A Methodology and a Comparative Studypt
dc.typeconferenceObjectpt
degois.publication.locationPadova, Italypt
degois.publication.title8th Sound and Music Computing Conference (SMC2011)pt
dc.peerreviewedyespt
dc.identifier.doi10.5281/zenodo.849887-
dc.identifier.doi10.5281/zenodo.849886-
dc.date.embargo2011-07-06*
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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This item is licensed under a Creative Commons License Creative Commons