The use of Wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices
AUTOR(ES)
Crovato, César David Paredes
DATA DE PUBLICAÇÃO
2011
RESUMO
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.
ASSUNTO(S)
redes neurais artificiais acoustical analysis of voices artificial neural network voz disfônica : transformadas wavelet dysphonic voice classification voz : patologia : análise acústica wavelet packet transform
ACESSO AO ARTIGO
http://hdl.handle.net/10183/27585Documentos Relacionados
- TRANFORMADA WAVELET E REDES NEURAIS ARTIFICIAIS NA ANÁLISE DE SINAIS RELACIONADOS À QUALIDADE DA ENERGIA ELÉTRICA
- Artificial neural networks in the classification and identification of soybean cultivars by planting region
- Automation in accession classification of Brazilian Capsicum germplasm through artificial neural networks
- Identificação de Sinais Radar Pulsados por Meio de Transformada de Wavelet Contínua e Redes Neurais Artificiais
- OUTFLOW FORECAST BASED ON ARTIFICIAL NEURAL NETORKS AND WAVELET TRANSFORM