Detection of Pathological Voices Using Discrete Wavelet Transform and Artificial Neural Networks

被引：0
|
作者：
Shia, S. Emerald [1 ]
Jayasree, T. [2 ]
机构：
[1] Cape Inst Technol, ECE Dept, Levengipuram, India
[2] Govt Coll Engn, ECE Dept, Tirunelveli, India
来源：
2017 IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNIQUES IN CONTROL, OPTIMIZATION AND SIGNAL PROCESSING (INCOS) | 2017年
关键词：
DWT-Discrete Wavelet Transform; FFNN-Feed Forward Neural Network; SVD-Sarbrueken Voice Data Base; IDENTIFICATION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The aim of this work is to develop an efficient voice disorder detection system using Discrete Wavelet Transform(DWT) and Feed Forward Neural Network (FFNN). In this experimental implementation the normal and abnormal utterances taken from Saarbrueken Voice Database (SVD) are subjected to 1-D Discrete Wavelet Decomposition and the energy of wavelet subband coefficients are computed. FFNN is finally used as a classifier to discriminate pathological voices from normal samples. The proposed system achieves 93.3% accuracy.
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页数：6
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