Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech
Contribuinte(s) |
Universidad EAFIT. Escuela de Ciencias. Grupo de Investigación Modelado Matemático dcampoc@eafit.edu.co oquinte1@eafit.edu.co mbastida@eafit.edu.co Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni (DITEN). Information and Signal Processing for Cognitive Telecommunications ISIP40, Genova, Italy Mathematical Modeling Research Group at Mathematical Sciences Department in School of Sciences at Universidad EAFIT, Medellín, Colombia |
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Data(s) |
2016
11/05/2016
2016
11/05/2016
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Resumo |
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 20th Argentinean Bioengineering Society Congress, SABI 2015 (XX Congreso Argentino de Bioingeniería y IX Jornadas de Ingeniería Clínica)28–30 October 2015, San Nicolás de los Arroyos, Argentina |
Formato |
application/pdf |
Identificador |
1742-6596 http://dx.doi.org/10.1088/1742-6596/705/1/012034 http://hdl.handle.net/10784/8374 10.1088/1742-6596/705/1/012034 |
Idioma(s) |
eng |
Publicador |
IOP Publishing |
Relação |
Journal of Physics: Conference Series; Vol. 705, Núm. 1 (2016); pp.7 http://dx.doi.org/10.1088/1742-6596/705/1/012034 |
Direitos |
info:eu-repo/semantics/openAccess openAccess Libre acceso Creative Commons Attribution 3.0 licence (CC BY 3.0) |
Fonte |
Journal of Physics: Conference Series; Vol. 705, Núm. 1 (2016); pp.7 |
Palavras-Chave | #Transformadas de Wavelet #Análisis Multi - Resolución #Procesamiento digital de voz #RECONOCIMIENTO AUTOMÁTICO DE LA VOZ #PROCESAMIENTO DE SEÑALES #INTELIGENCIA ARTIFICIAL #EMOCIONES #ANÁLISIS ESPECTRAL #REDES NEURALES (COMPUTADORES) #ANÁLISIS DE FOURIER #INTELIGENCIA ARTIFICIAL #Automatic speech recognition #Signal processing #Artificial intelligence #Emotions #Spectrum analysis #Neural networks (Computer science) #Fourier analysis #Artificial intelligence |
Tipo |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion article Artículo |