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We propose a study of the mathematical properties of voice as an audiosignal. This work includes signals in which the channel conditions are notideal for emotion recognition. Multiresolution analysis discrete wavelettransform was performed through the use of Daubechies Wavelet Family (Db1-Haar,Db 6, Db8, Db10) allowing the decomposition of the initial audio signal intosets of coefficients on which a set of features was extracted and analyzedstatistically in order to differentiate emotional states. ANNs proved to be asystem that allows an appropriate classification of such states. This studyshows that the extracted features using wavelet decomposition are enough toanalyze and extract emotional content in audio signals presenting a highaccuracy rate in classification of emotional states without the need to useother kinds of classical frequency-time features. Accordingly, this paper seeksto 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.

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