Examinando por Autor "Campo, D."

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  • No hay miniatura disponible
    Ítem
    Estudio de pilotos automáticos enfocados en la configuración de ala volante para vehículos aéreos no tripulados
    (Universidad EAFIT, 2014) Campo, D.; Benjumea, A.; Vélez, C. M.; Universidad EAFIT. Escuela de Ciencias. Grupo de Investigación Modelado Matemático
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    Ítem
    Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech
    (IOP Publishing, 2016) Campo, D.; Quintero, O.L.; Bastidas, M; Campo, D.; Quintero, O.L.; Bastidas, M; 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; Universidad EAFIT. Escuela de Ciencias; dcampoc@eafit.edu.co; oquinte1@eafit.edu.co; mbastida@eafit.edu.co; Modelado Matemático
    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
  • No hay miniatura disponible
    Ítem
    Multiresolution analysis (discrete wavelet transform) through Daubechies family for emotion recognition in speech.
    (IOP PUBLISHING LTD, 2016-01-01) Campo, D.; Quintero, O.L.; Bastidas, M.; Campo, D.; Quintero, O.L.; Bastidas, M.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    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.