Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/95166
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dc.contributor.authorRocha, Bruno-
dc.contributor.authorPanda, Renato-
dc.contributor.authorPaiva, Rui Pedro-
dc.date.accessioned2021-07-04T18:20:55Z-
dc.date.available2021-07-04T18:20:55Z-
dc.date.issued2013-
dc.identifier.urihttps://hdl.handle.net/10316/95166-
dc.description.abstractWe study the importance of a melodic audio (MA) feature set in music emotion recognition (MER) and compare its performance to an approach using only standard audio (SA) features. We also analyse the fusion of both types of features. Employing only SA features, the best attained performance was 46.3%, while using only MA features the best outcome was 59.1% (F- measure). A combination of SA and MA features improved results to 64%. These results might have an important impact to help break the so-called glass ceiling in MER, as most current approaches are based on SA features.pt
dc.description.sponsorshipThis work was supported by the MOODetector project (PTDC/EIA-EIA/102185/2008), financed by the Fundação para Ciência e a Tecnologia (FCT) and Programa Operacional Temático Factores de Competitividade (COMPETE) - 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.subjectaudiopt
dc.subjectmachine learningpt
dc.subjectmelodic featurespt
dc.subjectmusic emotion recognitionpt
dc.titleMusic Emotion Recognition: The Importance of Melodic Featurespt
dc.typeconferenceObjectpt
degois.publication.locationPrague, Czech Republicpt
degois.publication.title6th International Workshop on Music and Machine Learning – MML 2013 – in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases – ECML/PKDD 2013pt
dc.peerreviewedyespt
dc.date.embargo2013-01-01*
uc.date.periodoEmbargo0pt
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeconferenceObject-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextCom Texto completo-
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.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.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.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0003-1643-667X-
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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