Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/94225
DC FieldValueLanguage
dc.contributor.authorPanda, Renato-
dc.contributor.authorMalheiro, Ricardo-
dc.contributor.authorPaiva, Rui Pedro-
dc.date.accessioned2021-04-15T08:32:04Z-
dc.date.available2021-04-15T08:32:04Z-
dc.date.issued2018-
dc.identifier.urihttps://hdl.handle.net/10316/94225-
dc.description.abstractWe present a set of novel emotionally-relevant audio features to help improving the classification of emotions in audio music. First, a review of the state-of-the-art regarding emotion and music was conducted, to understand how the various music concepts may influence human emotions. Next, well known audio frameworks were analyzed, assessing how their extractors relate with the studied musical concepts. The intersection of this data showed an unbalanced representation of the eight musical concepts. Namely, most extractors are low-level and related with tone color, while musical form, musical texture and expressive techniques are lacking. Based on this, we developed a set of new algorithms to capture information related with musical texture and expressive techniques, the two most lacking concepts. To validate our work, a public dataset containing 900 30-second clips, annotated in terms of Russell’s emotion quadrants was created. The inclusion of our features improved the F1-score obtained using the best 100 features by 8.6% (to 76.0%), using support vector machines and 20 repetitions of 10-fold cross-validation.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.subjectMusical texturept
dc.subjectExpressive techniquespt
dc.subjectMusic emotion recognitionpt
dc.subjectAffective computingpt
dc.titleMusical Texture and Expressivity Features for Music Emotion Recognitionpt
dc.typeconferenceObjectpt
degois.publication.firstPage383pt
degois.publication.lastPage391pt
degois.publication.locationParis (France)pt
degois.publication.title19th International Society for Music Information Retrieval Conference (ISMIR 2018)pt
dc.date.updated2021-04-14T21:13:34Z-
dc.peerreviewedyespt
dc.description.version661A-31CC-8D19 | Renato Panda-
dc.description.versioninfo:eu-repo/semantics/publishedVersion-
dc.identifier.slugcv-prod-683716-
dc.date.embargo2018-01-01*
uc.date.periodoEmbargo0pt
item.openairetypeconferenceObject-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextCom Texto completo-
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.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.orcid0000-0003-2539-5590-
crisitem.author.orcid0000-0002-3010-2732-
crisitem.author.orcid0000-0003-3215-3960-
crisitem.project.grantnoinfo:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Music-
Appears in Collections:I&D CISUC - Artigos em Livros de Actas
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