2016-05-1120161742-6596http://hdl.handle.net/10784/8374We 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 identifyapplication/pdfengTransformadas de WaveletAnálisis Multi - ResoluciónProcesamiento digital de vozinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccessRECONOCIMIENTO AUTOMÁTICO DE LA VOZPROCESAMIENTO DE SEÑALESINTELIGENCIA ARTIFICIALEMOCIONESANÁLISIS ESPECTRALREDES NEURALES (COMPUTADORES)ANÁLISIS DE FOURIERINTELIGENCIA ARTIFICIALAutomatic speech recognitionSignal processingArtificial intelligenceEmotionsSpectrum analysisNeural networks (Computer science)Fourier analysisArtificial intelligenceAutomatic speech recognitionSignal processingArtificial intelligenceEmotionsSpectrum analysisNeural networks (Computer science)Fourier analysisArtificial intelligenceTransformadas de WaveletAnálisis Multi - ResoluciónProcesamiento digital de vozAcceso abierto2016-05-11Creative Commons Attribution 3.0 licence (CC BY 3.0)Campo, D.Quintero, O.L.Bastidas, MCampo, D.Quintero, O.L.Bastidas, M10.1088/1742-6596/705/1/012034