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《Audio Engineering》 2014-03
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Identification of Pathological Voice Complexity Based on Neural Network

GONG Yingji;Department of Physics and Mechanical & Electronic Engineering,Hechi University;  
Based on complexity of voice system and pathological voice's noise character in high- frequency section,firstly,wavelet decomposition,reconstruction for normal,pathological voice signals is used; secsend compute Lempel- Zvi complexity for each band signal that reconstructed; then neural networks are used to identificate Lempel-Zvi complexity of each band signal. The result shows that,in high-frequency section,The complexity of pathological voice recognition rate is 84%, relatively higher than that of other low frequency. The pattern recognition method reveals the noise characteristics and complexity of voice system lesions.
【Fund】: 河池学院青年课题资助项目(2012B-N002)
【CateGory Index】: TN912.34
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