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Laryngeal pathologies directly affect the quality of the voice. In the last years, digital signal processing techniques have been applied for detection of vocal fold pathologies through speech signal analysis. In this work, a binary Particle Swarm Optimization (PSO) algorithm using Multilayer Perceptron (MLP) neural network is employed for the selection of the most significative features in a pathological...
This work proposes an efficient texture classification strategy performed in the wavelet domain in order to characterize healthy and pathological speech signals from recurrence plots (RP). The two-dimensional wavelet transform is applied to the recurrence plots at one resolution level. Thirteen Haralick texture features are obtained from each approximation and detail subband coefficients. In classification,...
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