The use of wavelet packet transform and artificial neural networks in analysis and classification of dysphonic voices.
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.
Crovato
César David Paredes
CD
Departamento de Engenharia Elétrica, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, CEP 90.035-190, Brazil. cesarcrovato@yahoo.com.br
Schuck
Adalberto
A
D000465:Algorithms; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography / Q000379:methods*; D014832:Voice Disorders / Q000175:diagnosis*