We propose a study of the mathematical properties of voice as an audio signal. This\nwork includes signals in which the channel conditions are not ideal for emotion recognition.\nMulti resolution analysis- discrete wavelet transform ââ?¬â?? was performed through the use of\nDaubechies Wavelet Family (Db1-Haar, Db 6, Db8, Db10) allowing the decomposition of the\ninitial audio signal into sets of coefficients on which a set of features was extracted and\nanalyzed statistically in order to differentiate emotional states. ANNs proved to be a system\nthat allows an appropriate classification of such states. This study shows that the extracted\nfeatures using wavelet decomposition are enough to analyze and extract emotional content in\naudio signals presenting a high accuracy rate in classification of emotional states without the\nneed to use other kinds of classical frequency-time features. Accordingly, this paper seeks to\ncharacterize mathematically the six basic emotions in humans: boredom, disgust, happiness,\nanxiety, anger and sadness, also included the neutrality, for a total of seven states to identify.
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