Double Fourier analysis for Emotion Identification in Voiced Speech

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dc.citation.epage6
dc.citation.issue1
dc.citation.journalTitleJournal of Physics: Conference Serieseng
dc.citation.spage1
dc.citation.volume705
dc.contributor.affiliationMathematical Modeling Research Group, GRIMMAT, School of Sciences, Universidad EAFIT, Medellín, Colombiaspa
dc.contributor.authorSierra-Sosa, Dspa
dc.contributor.authorBastidas, Mspa
dc.contributor.authorOrtiz P., D.spa
dc.contributor.authorQuintero, O.L.spa
dc.contributor.authorSierra-Sosa, D
dc.contributor.authorBastidas, M
dc.contributor.authorOrtiz P., D.
dc.contributor.authorQuintero, O.L.
dc.contributor.departmentUniversidad EAFIT. Escuela de Cienciasspa
dc.contributor.eafitauthordsierras@eafit.edu.co
dc.contributor.researchgroupModelado Matemáticospa
dc.date2016
dc.date.accessioned2016-05-11T20:44:10Z
dc.date.available2016-05-11T20:44:10Z
dc.date.issued2016
dc.description.abstractWe propose a novel analysis alternative, based on two Fourier Transforms for emotion recognition from speech -- Fourier analysis allows for display and synthesizes different signals, in terms of power spectral density distributions -- A spectrogram of the voice signal is obtained performing a short time Fourier Transform with Gaussian windows, this spectrogram portraits frequency related features, such as vocal tract resonances and quasi-periodic excitations during voiced sounds -- Emotions induce such characteristics in speech, which become apparent in spectrogram time-frequency distributions -- Later, the signal time-frequency representation from spectrogram is considered an image, and processed through a 2-dimensional Fourier Transform in order to perform the spatial Fourier analysis from it -- Finally features related with emotions in voiced speech are extracted and presentedeng
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.doi10.1088/1742-6596/705/1/012035
dc.identifier.issn1742-6596
dc.identifier.urihttp://hdl.handle.net/10784/8375
dc.language.isoengeng
dc.publisherIOP Publishing
dc.relation.ispartofJournal of Physics: Conference Series; Vol. 705, Núm. 1 (2016); pp.9spa
dc.relation.isversionofhttp://dx.doi.org/10.1088/1742-6596/705/1/012035
dc.relation.urihttp://dx.doi.org/10.1088/1742-6596/705/1/012035
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.subjectProcesamiento digital de voz
dc.subjectMorfología matemática
dc.subject.keywordSpectrum analysis
dc.subject.keywordFourier analysis
dc.subject.keywordSignal processing
dc.subject.keywordSpeech processing systems
dc.subject.keywordTransformations (mathematics)
dc.subject.keywordHeisenberg uncertainty principle
dc.subject.keywordSpectrum analysiseng
dc.subject.keywordFourier analysiseng
dc.subject.keywordSignal processingeng
dc.subject.keywordSpeech processing systemseng
dc.subject.keywordTransformations (mathematics)eng
dc.subject.keywordHeisenberg uncertainty principleeng
dc.subject.keywordTransformadas de Waveletspa
dc.subject.keywordProcesamiento digital de vozspa
dc.subject.keywordMorfología matemáticaspa
dc.subject.lembANÁLISIS ESPECTRAL
dc.subject.lembANÁLISIS DE FOURIER
dc.subject.lembPROCESAMIENTO DE SEÑALES
dc.subject.lembSISTEMAS DE PROCESAMIENTO DE LA VOZ
dc.subject.lembTRANSFORMACIONES (MATEMÁTICAS)
dc.subject.lembPRINCIPIO DE INCERTIDUMBRE DE HEISENBERG
dc.titleDouble Fourier analysis for Emotion Identification in Voiced 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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