Examinando por Autor "Quintero, O.L."

Mostrando 1 - 9 de 9
  • No hay miniatura disponible
    Ítem
    Artificial Intelligence for Modelling Dengue Transmission in Bello
    (Universidad EAFIT, 2014) Lobo, Elisabet; Quintero, O.L.; Universidad EAFIT. Escuela de Ciencias. Grupo de Investigación Modelado Matemático
  • No hay miniatura disponible
    Ítem
    Comparison on the estimation of the biomass of a batch bioreactor through fuzzy systems, neural networks and adaptive neuro-fuzzy inference system
    (2011-01-01) Muñoz, A.A.G.; Quintero, O.L.; Muñoz, A.A.G.; Quintero, O.L.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    The estimation of biomass production of d-endotoxins of the Bacillus thuringiensis (Bt) is a major problem in biotechnological processes, as bio-insecticides, which has been addressed with different methodologies such as extended Kalman filters (EKF), phenomenological observers, among others. This paper presents a comparison in the estimation of biomass concentration of d - endotoxins of the Bacillus thuringiensis (Bt), using Mamdani fuzzy inference systems (FIS), neural networks (NN) and adaptive neuro-fuzzy inference system (ANFIS) trained with differents clustering algorithms; and comparing the associated outcomes among these. © 2011 IEEE.
  • No hay miniatura disponible
    Ítem
    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
  • No hay miniatura disponible
    Ítem
    Double Fourier analysis for Emotion Identification in Voiced Speech
    (IOP PUBLISHING LTD, 2016-01-01) Sierra-Sosa, D.; Bastidas, M.; Ortiz, P.D.; Quintero, O.L.; Sierra-Sosa, D.; Bastidas, M.; Ortiz, P.D.; Quintero, O.L.; Universidad EAFIT. Departamento de Ciencias; 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.
  • No hay miniatura disponible
    Ítem
    Metodología para Detección de Características Faciales con Fines de Reconocimiento de Emociones
    (Universidad EAFIT, 2014) Gonzalez, C.; Rincon, S.; Quintero, O.L.; Restrepo, R.; Universidad EAFIT. Escuela de Ciencias. Grupo de Investigación Modelado Matemático
    It is believed that the detection of emotions could lead to determine the mood of a person or even a possible fraud. The detection of key facial features to detect emotions are of easy recognition for humans, but the diffculty increases when is done by software. For this reason, this investigation addresses the problem of detection of emotions through several techniques, identifying one in particular based on the golden proportions, which strengthens the detection of facial features and therefore the detection of emotion, keeping rational measures of uncertainty.
  • No hay miniatura disponible
    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.
  • No hay miniatura disponible
    Ítem
    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
  • No hay miniatura disponible
    Ítem
    A simple but efficient voice activity detection algorithm through Hilbert transform and dynamic threshold for speech pathologies
    (IOP PUBLISHING LTD, 2016-01-01) Ortiz, P.D.; Villa, L.F.; Salazar, C.; Quintero, O.L.; Ortiz, P.D.; Villa, L.F.; Salazar, C.; Quintero, O.L.; Universidad EAFIT. Departamento de Ciencias; 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.