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

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JPCS_705_1_012034.pdf (743.0Kb)
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2016
Author
Campo, D.
Quintero, O.L.
Bastidas, M
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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
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http://dx.doi.org/10.1088/1742-6596/705/1/012034
http://hdl.handle.net/10784/8374
Editor URL
Journal of Physics: Conference Series; Vol. 705, Núm. 1 (2016); pp.7
DOI
10.1088/1742-6596/705/1/012034
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