|Título||The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices
Crovato, César David Paredes
Schuck Junior, Adalberto
|Abstract||This paper presents a dysphonic voice classification system using the wavelet packet transform and the best basis algorithm (BBA) as dimensionality reductor and 06 artificial neural networks (ANN) acting as specialist systems. Each ANN was a 03-layer multilayer perceptron with 64 input nodes, 01 output node and in the intermediary layer the number of neurons depends on the related training pathology group. The dysphonic voice database was separated in five pathology groups and one healthy control group. Each ANN was trained and associated with one of the 06 groups, and fed by the best base tree (BBT) nodes’ entropy values, using the multiple cross validation (MCV) method and the leave-one-out (LOO) variation technique and success rates obtained were 87.5%, 95.31%, 87.5%, 100%, 96.87% and 89.06% for the groups 01 to 06, respectively.
|Contido em||IEEE transactions on biomedical engineering. New York, NY. vol. 54, no. 10 (oct. 2007), p. 1898-1900.
Processamento de sinais de voz
Redes neurais artificiais
[en] Acoustical analysis of voices
[en] Artificial neural network
[en] Dysphonic voice classification
[en] Wavelet packet transform
|Tipo||Artigo de periódico
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