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Feature selection for place classification through environmental sounds

dc.creatorRamón Brena Pinero
dc.date2014
dc.date.accessioned2018-10-18T20:34:49Z
dc.date.available2018-10-18T20:34:49Z
dc.identifier.issn18770509
dc.identifier.doi10.1016/j.procs.2014.08.010
dc.identifier.urihttp://hdl.handle.net/11285/630367
dc.descriptionIn this work, an environmental audio classification scheme is proposed using a Chi squared filter as a feature selection strategy. Using feature selection (FS), the original 62 features characteristic vector can be optimized, and it can be used for environmental sound classification. These features are obtained using statistical analysis and frequency domain analysis. As a result, we obtain a reduced feature vector composed of 15 features: 11 statistical and 4 of the frequency domain. Using this reduced vector, a 10 class classification was done, using Support Vector machines (SVM) as classification method, the accuracy is higher than 90%. © 2014 The Authors.
dc.languageeng
dc.publisherElsevier B.V.
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84930334320&doi=10.1016%2fj.procs.2014.08.010&partnerID=40&md5=0a6274f01bbaedbc196672331e577cfe
dc.relationInvestigadores
dc.relationEstudiantes
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.sourceProcedia Computer Science
dc.subject.classification7 INGENIERÍA Y TECNOLOGÍA
dc.titleFeature selection for place classification through environmental sounds
dc.typeConferencia
dc.identifier.volume37
dc.identifier.startpage40
dc.identifier.endpage47
refterms.dateFOA2018-10-18T20:34:49Z


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