Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech

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dc.citation.epage7
dc.citation.issue1
dc.citation.journalTitleJournal of Physics: Conference Serieseng
dc.citation.spage1
dc.citation.volume705
dc.contributor.affiliationDipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni (DITEN). Information and Signal Processing for Cognitive Telecommunications ISIP40, Genova, Italyspa
dc.contributor.affiliationMathematical Modeling Research Group at Mathematical Sciences Department in School of Sciences at Universidad EAFIT, Medellín, Colombiaspa
dc.contributor.authorCampo, D.spa
dc.contributor.authorQuintero, O.L.spa
dc.contributor.authorBastidas, Mspa
dc.contributor.authorCampo, D.
dc.contributor.authorQuintero, O.L.
dc.contributor.authorBastidas, M
dc.contributor.departmentUniversidad EAFIT. Escuela de Cienciasspa
dc.contributor.eafitauthordcampoc@eafit.edu.co
dc.contributor.eafitauthoroquinte1@eafit.edu.co
dc.contributor.eafitauthormbastida@eafit.edu.co
dc.contributor.researchgroupModelado Matemáticospa
dc.date2016
dc.date.accessioned2016-05-11T20:44:09Z
dc.date.available2016-05-11T20:44:09Z
dc.date.issued2016
dc.description.abstractWe propose a study of the mathematical properties of voice as an audio signal -- This work includes signals in which the channel conditions are not ideal for emotion recognition -- Multiresolution analysis- discrete wavelet transform – was performed through the use of Daubechies Wavelet Family (Db1-Haar, Db6, Db8, Db10) allowing the decomposition of the initial audio signal into sets of coefficients on which a set of features was extracted and analyzed statistically in order to differentiate emotional states -- ANNs proved to be a system that allows an appropriate classification of such states -- This study shows that the extracted features using wavelet decomposition are enough to analyze and extract emotional content in audio signals presenting a high accuracy rate in classification of emotional states without the need to use other kinds of classical frequency-time features -- Accordingly, this paper seeks to characterize mathematically the six basic emotions in humans: boredom, disgust, happiness, anxiety, anger and sadness, also included the neutrality, for a total of seven states to identifyeng
dc.description.note20th Argentinean Bioengineering Society Congress, SABI 2015 (XX Congreso Argentino de Bioingeniería y IX Jornadas de Ingeniería Clínica)28–30 October 2015, San Nicolás de los Arroyos, Argentinaeng
dc.formatapplication/pdf
dc.identifier.doi10.1088/1742-6596/705/1/012034
dc.identifier.issn1742-6596
dc.identifier.urihttp://hdl.handle.net/10784/8374
dc.language.isoengeng
dc.publisherIOP Publishing
dc.relation.ispartofJournal of Physics: Conference Series; Vol. 705, Núm. 1 (2016); pp.7spa
dc.relation.isversionofhttp://dx.doi.org/10.1088/1742-6596/705/1/012034
dc.relation.urihttp://dx.doi.org/10.1088/1742-6596/705/1/012034
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
dc.rights.licenseCreative Commons Attribution 3.0 licence (CC BY 3.0)eng
dc.rights.localAcceso abiertospa
dc.sourceJournal of Physics: Conference Series
dc.subjectTransformadas de Wavelet
dc.subjectAnálisis Multi - Resolución
dc.subjectProcesamiento digital de voz
dc.subject.keywordAutomatic speech recognition
dc.subject.keywordSignal processing
dc.subject.keywordArtificial intelligence
dc.subject.keywordEmotions
dc.subject.keywordSpectrum analysis
dc.subject.keywordNeural networks (Computer science)
dc.subject.keywordFourier analysis
dc.subject.keywordArtificial intelligence
dc.subject.keywordAutomatic speech recognitioneng
dc.subject.keywordSignal processingeng
dc.subject.keywordArtificial intelligenceeng
dc.subject.keywordEmotionseng
dc.subject.keywordSpectrum analysiseng
dc.subject.keywordNeural networks (Computer science)eng
dc.subject.keywordFourier analysiseng
dc.subject.keywordArtificial intelligenceeng
dc.subject.keywordTransformadas de Waveletspa
dc.subject.keywordAnálisis Multi - Resoluciónspa
dc.subject.keywordProcesamiento digital de vozspa
dc.subject.lembRECONOCIMIENTO AUTOMÁTICO DE LA VOZ
dc.subject.lembPROCESAMIENTO DE SEÑALES
dc.subject.lembINTELIGENCIA ARTIFICIAL
dc.subject.lembEMOCIONES
dc.subject.lembANÁLISIS ESPECTRAL
dc.subject.lembREDES NEURALES (COMPUTADORES)
dc.subject.lembANÁLISIS DE FOURIER
dc.subject.lembINTELIGENCIA ARTIFICIAL
dc.titleMultiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speecheng
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typearticle
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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