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

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dc.contributor.authorCampo, D.
dc.contributor.authorQuintero, O.L.
dc.contributor.authorBastidas, M.
dc.contributor.departmentUniversidad EAFIT. Departamento de Cienciasspa
dc.contributor.researchgroupModelado Matemáticospa
dc.creatorCampo, D.
dc.creatorQuintero, O.L.
dc.creatorBastidas, M.
dc.date.accessioned2021-04-12T14:11:48Z
dc.date.available2021-04-12T14:11:48Z
dc.date.issued2016-01-01
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 identify.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=4295
dc.identifier.doi10.1088/1742-6596/705/1/012034
dc.identifier.issn17426596
dc.identifier.otherSCOPUS;2-s2.0-84978129665
dc.identifier.urihttp://hdl.handle.net/10784/27894
dc.language.isoengeng
dc.publisherIOP PUBLISHING LTD
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84978129665&doi=10.1088%2f1742-6596%2f705%2f1%2f012034&partnerID=40&md5=6c1042dd0bb8f266fb6d830f8a6fc05e
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1742-6596
dc.sourceJournal Of Physics: Conference Series
dc.subject.keywordAudio signal processingeng
dc.subject.keywordBiomedical engineeringeng
dc.subject.keywordDiscrete wavelet transformseng
dc.subject.keywordMultiresolution analysiseng
dc.subject.keywordWavelet decompositioneng
dc.subject.keywordWavelet transformseng
dc.subject.keywordBasic emotionseng
dc.subject.keywordChannel conditionseng
dc.subject.keywordDaubechies Waveleteng
dc.subject.keywordEmotion recognitioneng
dc.subject.keywordEmotional stateeng
dc.subject.keywordHigh-accuracyeng
dc.subject.keywordMathematical propertieseng
dc.subject.keywordTime featureseng
dc.subject.keywordSpeech recognitioneng
dc.titleMultiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech.eng
dc.typeinfo:eu-repo/semantics/conferencePapereng
dc.typeconferencePapereng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
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