Examinando por Materia "Wavelet transforms"

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    Application of the continuous wavelet transform in the extraction of directional data on RTM imaging condition wavefields
    (Ecopetrol, 2018-01-01) Paniagua-Castrillón J.-G.; Quintero-Montoya O.-L.; Universidad EAFIT. Escuela de Ciencias; Modelado Matemático
    Low-frequency artifacts in reverse time migration result from unwanted cross-correlation of the source and receiver wavefields at nonreflecting points along ray-paths. These artifacts can hide important details in migrated models and increase poor interpretation risk. Some methods have been proposed to avoid or reduce the number of these artifacts, preserving reflections, and improving model quality, implementing other strategies such as modification of the wave equation, proposing other imaging conditions, and using image filtering techniques. One of these methods uses wavefield decomposition, correlating components of the wavefields that propagate in opposite directions. We propose a method for extracting directional information from the RTM imaging condition wavefields to obtain characteristics allowing for better, more refined imaging. The method works by separating directional information about the wavefields based on the continuous wavelet transform (CWT), and the analysis of the main changes on the frequency content revealed within the scalogram obtained by a Gaussian wavelet family. Through numerical applications, we demonstrate that this method can effectively remove undesired artifacts in migrated images. In addition, we use the Laguerre-Gauss filtering to improve the results obtained with the proposed method. © 2018 Ecopetrol S.A. All Rights Reserved.
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    Evaluation of wavelet measures on automatic detection of emotion in noisy and telephony speech signals
    (IEEE, 2014-01-01) Vasquez-Correa, J. C.; Garcia, N.; Vargas-Bonilla, J. F.; Orozco-Arroyave, J. R.; Arias-Londono, J. D.; Lucia Quintero M, O.; Vasquez-Correa, J. C.; Garcia, N.; Vargas-Bonilla, J. F.; Orozco-Arroyave, J. R.; Arias-Londono, J. D.; Lucia Quintero M, O.; Universidad EAFIT. Departamento de Ciencias; Modelado Matemático
    Detection of emotion in humans from speech signals is a recent research field. One of the scenarios where this field has been applied is in situations where the human integrity and security are at risk. In this paper we are propossing a set of features based on the Teager energy operator, and several entropy measures obtained from the decomposition signals from discrete wavelet transform to characterize different types of negative emotions such as anger, anxiety, disgust, and desperation. The features are measured in three different conditions: (1) the original speech signals, (2) the signals that are contaminated with noise, or are affected by the presence of a phone channel, and (3) the signals that are obtained after processing using an algorithm for Speech Enhancement based on Karhunen-Love Transform. According to the results, when the speech enhancement is applied, the detection of emotion in speech is increased in up to 22% compared to results obtained when the speech signal is highly contaminated with noise. © 2014 IEEE.
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    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.