  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,
Db 6, 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.
