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https://hdl.handle.net/10784/1512

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Double Fourier analysis for Emotion Identification in Voiced Speech
(IOP Publishing, 2016) Sierra-Sosa, D; Bastidas, M; Ortiz P., D.; Quintero, O.L.; Sierra-Sosa, D; Bastidas, M; Ortiz P., D.; Quintero, O.L.; Mathematical Modeling Research Group, GRIMMAT, School of Sciences, Universidad EAFIT, Medellín, Colombia; Universidad EAFIT. Escuela de Ciencias; dsierras@eafit.edu.co; Modelado Matemático
We propose a novel analysis alternative, based on two Fourier Transforms for emotion recognition from speech -- Fourier analysis allows for display and synthesizes different signals, in terms of power spectral density distributions -- A spectrogram of the voice signal is obtained performing a short time Fourier Transform with Gaussian windows, this spectrogram portraits frequency related features, such as vocal tract resonances and quasi-periodic excitations during voiced sounds -- Emotions induce such characteristics in speech, which become apparent in spectrogram time-frequency distributions -- Later, the signal time-frequency representation from spectrogram is considered an image, and processed through a 2-dimensional Fourier Transform in order to perform the spatial Fourier analysis from it -- Finally features related with emotions in voiced speech are extracted and presented
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
A simple but efficient voice activity detection algorithm through Hilbert transform and dynamic threshold for speech pathologies
(IOP Publishing, 2016) Ortiz P., D.; Villa, Luisa F.; Salazar, Carlos; Quintero, O.L.; Ortiz P., D.; Villa, Luisa F.; Salazar, Carlos; Quintero, O.L.; Mathematical Modeling Research Group, GRIMMAT, School of Sciences, Universidad EAFIT, Medellín, Colombia; Universidad EAFIT. Escuela de Ciencias; dpuerta1@eafit.edu.co; oquinte1@eafit.edu.co; Modelado Matemático
A simple but efficient voice activity detector based on the Hilbert transform and a dynamic threshold is presented to be used on the pre-processing of audio signals -- The algorithm to define the dynamic threshold is a modification of a convex combination found in literature -- This scheme allows the detection of prosodic and silence segments on a speech in presence of non-ideal conditions like a spectral overlapped noise -- The present work shows preliminary results over a database built with some political speech -- The tests were performed adding artificial noise to natural noises over the audio signals, and some algorithms are compared -- Results will be extrapolated to the field of adaptive filtering on monophonic signals and the analysis of speech pathologies on futures works

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