New trends in musical genre classification using optimum-path forest

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dc.contributor.authorMarques, C. [UNESP]
dc.contributor.authorGuilherme, Ivan Rizzo [UNESP]
dc.contributor.authorNakamura, R. Y M [UNESP]
dc.contributor.authorPapa, João Paulo [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:26:16Z
dc.date.available2014-05-27T11:26:16Z
dc.date.issued2011-12-01
dc.description.abstractMusical genre classification has been paramount in the last years, mainly in large multimedia datasets, in which new songs and genres can be added at every moment by anyone. In this context, we have seen the growing of musical recommendation systems, which can improve the benefits for several applications, such as social networks and collective musical libraries. In this work, we have introduced a recent machine learning technique named Optimum-Path Forest (OPF) for musical genre classification, which has been demonstrated to be similar to the state-of-the-art pattern recognition techniques, but much faster for some applications. Experiments in two public datasets were conducted against Support Vector Machines and a Bayesian classifier to show the validity of our work. In addition, we have executed an experiment using very recent hybrid feature selection techniques based on OPF to speed up feature extraction process. © 2011 International Society for Music Information Retrieval.en
dc.description.affiliationDep. of Statistics, Applied Math. and Computation Universidade Estadual Paulista (UNESP), Rio Claro, SP
dc.description.affiliationDepartment of Computing Universidade Estadual Paulista (UNESP), Bauru, SP
dc.description.affiliationUnespDep. of Statistics, Applied Math. and Computation Universidade Estadual Paulista (UNESP), Rio Claro, SP
dc.description.affiliationUnespDepartment of Computing Universidade Estadual Paulista (UNESP), Bauru, SP
dc.format.extent699-704
dc.identifierhttp://ismir2011.ismir.net/papers/PS6-8.pdf
dc.identifier.citationProceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011, p. 699-704.
dc.identifier.lattes0140365057016044
dc.identifier.scopus2-s2.0-84873575554
dc.identifier.urihttp://hdl.handle.net/11449/72943
dc.language.isoeng
dc.relation.ispartofProceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011
dc.rights.accessRightsAcesso aberto
dc.sourceScopus
dc.subjectBayesian classifier
dc.subjectHybrid feature selections
dc.subjectMachine learning techniques
dc.subjectMusical genre classification
dc.subjectOptimum-path forests
dc.subjectPattern recognition techniques
dc.subjectSocial Networks
dc.subjectSpeed up
dc.subjectExperiments
dc.subjectFeature extraction
dc.subjectForestry
dc.subjectInformation retrieval
dc.subjectLearning systems
dc.subjectClassification (of information)
dc.subjectClassification
dc.subjectExperimentation
dc.subjectInformation Retrieval
dc.titleNew trends in musical genre classification using optimum-path foresten
dc.typeTrabalho apresentado em evento
unesp.author.lattes0140365057016044
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Geociências e Ciências Exatas, Rio Claropt
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
unesp.departmentComputação - FCpt

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Artigos - Estatistica, Matemática Aplicada e Computação - IGCE
Bauru - FC - Faculdade de Ciências