Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech
dc.citation.epage | 7 | |
dc.citation.issue | 1 | |
dc.citation.journalTitle | Journal of Physics: Conference Series | eng |
dc.citation.spage | 1 | |
dc.citation.volume | 705 | |
dc.contributor.affiliation | Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni (DITEN). Information and Signal Processing for Cognitive Telecommunications ISIP40, Genova, Italy | spa |
dc.contributor.affiliation | Mathematical Modeling Research Group at Mathematical Sciences Department in School of Sciences at Universidad EAFIT, Medellín, Colombia | spa |
dc.contributor.author | Campo, D. | spa |
dc.contributor.author | Quintero, O.L. | spa |
dc.contributor.author | Bastidas, M | spa |
dc.contributor.author | Campo, D. | |
dc.contributor.author | Quintero, O.L. | |
dc.contributor.author | Bastidas, M | |
dc.contributor.department | Universidad EAFIT. Escuela de Ciencias | spa |
dc.contributor.eafitauthor | dcampoc@eafit.edu.co | |
dc.contributor.eafitauthor | oquinte1@eafit.edu.co | |
dc.contributor.eafitauthor | mbastida@eafit.edu.co | |
dc.contributor.researchgroup | Modelado Matemático | spa |
dc.date | 2016 | |
dc.date.accessioned | 2016-05-11T20:44:09Z | |
dc.date.available | 2016-05-11T20:44:09Z | |
dc.date.issued | 2016 | |
dc.description.abstract | We 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.description.note | 20th 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, Argentina | eng |
dc.format | application/pdf | |
dc.identifier.doi | 10.1088/1742-6596/705/1/012034 | |
dc.identifier.issn | 1742-6596 | |
dc.identifier.uri | http://hdl.handle.net/10784/8374 | |
dc.language.iso | eng | eng |
dc.publisher | IOP Publishing | |
dc.relation.ispartof | Journal of Physics: Conference Series; Vol. 705, Núm. 1 (2016); pp.7 | spa |
dc.relation.isversionof | http://dx.doi.org/10.1088/1742-6596/705/1/012034 | |
dc.relation.uri | http://dx.doi.org/10.1088/1742-6596/705/1/012034 | |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | eng |
dc.rights.license | Creative Commons Attribution 3.0 licence (CC BY 3.0) | eng |
dc.rights.local | Acceso abierto | spa |
dc.source | Journal of Physics: Conference Series | |
dc.subject | Transformadas de Wavelet | |
dc.subject | Análisis Multi - Resolución | |
dc.subject | Procesamiento digital de voz | |
dc.subject.keyword | Automatic speech recognition | |
dc.subject.keyword | Signal processing | |
dc.subject.keyword | Artificial intelligence | |
dc.subject.keyword | Emotions | |
dc.subject.keyword | Spectrum analysis | |
dc.subject.keyword | Neural networks (Computer science) | |
dc.subject.keyword | Fourier analysis | |
dc.subject.keyword | Artificial intelligence | |
dc.subject.keyword | Automatic speech recognition | eng |
dc.subject.keyword | Signal processing | eng |
dc.subject.keyword | Artificial intelligence | eng |
dc.subject.keyword | Emotions | eng |
dc.subject.keyword | Spectrum analysis | eng |
dc.subject.keyword | Neural networks (Computer science) | eng |
dc.subject.keyword | Fourier analysis | eng |
dc.subject.keyword | Artificial intelligence | eng |
dc.subject.keyword | Transformadas de Wavelet | spa |
dc.subject.keyword | Análisis Multi - Resolución | spa |
dc.subject.keyword | Procesamiento digital de voz | spa |
dc.subject.lemb | RECONOCIMIENTO AUTOMÁTICO DE LA VOZ | |
dc.subject.lemb | PROCESAMIENTO DE SEÑALES | |
dc.subject.lemb | INTELIGENCIA ARTIFICIAL | |
dc.subject.lemb | EMOCIONES | |
dc.subject.lemb | ANÁLISIS ESPECTRAL | |
dc.subject.lemb | REDES NEURALES (COMPUTADORES) | |
dc.subject.lemb | ANÁLISIS DE FOURIER | |
dc.subject.lemb | INTELIGENCIA ARTIFICIAL | |
dc.title | Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech | eng |
dc.type | info:eu-repo/semantics/article | |
dc.type | info:eu-repo/semantics/publishedVersion | |
dc.type | article | |
dc.type | article | eng |
dc.type | info:eu-repo/semantics/article | eng |
dc.type | info:eu-repo/semantics/publishedVersion | eng |
dc.type | publishedVersion | eng |
dc.type.local | Artículo | spa |
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