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

20th Argentinean Bioengineering Society Congress

物理学;生物科学

Campo, D.^1,2 ; Quintero, O.L.^2 ; Bastidas, M.^2

Dipartimento di Ingegneria Navale Elettrica Elettronica e Delle Telecomunicazioni (DITEN), Information and Signal Processing for Cognitive Telecommunications, Genova

ISIP40, Italy^1

Mathematical Modeling Research Group, Mathematical Sciences Department in School of Sciences, Universidad EAFIT, Medellin, Colombia^2

关键词: Basic emotions;    Channel conditions;    Daubechies Wavelet;    Emotion recognition;    Emotional state;    High-accuracy;    Mathematical properties;    Time features;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/705/1/012034/pdf
DOI  :  10.1088/1742-6596/705/1/012034

学科分类：生物科学（综合）

来源: IOP

【 摘 要 】

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.