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• W2896755317 abstract "Voice pathology correlates with vocal fold problems, so extracting valid features from the vocal fold excitation signal is helpful for classifying the normal and pathological voice. A novel feature extraction method which combines wavelet packet decomposition and nonlinear feature extraction is proposed in this paper. The original speech signals are firstly decomposed into 5 layers using wavelet packet-based method, and the high frequency signals which correlate with the vocal fold are reconstructed. Then nonlinear features are extracted from the reconstructed signals. Support Vector Machine is used to classify the normal and pathological voice using the nonlinear features. The proposed method and features are evaluated on the Massachusetts Eye and Ear Infirmary databases. The second-order renyi entropy features give very promising classification accuracy of 98.21%. The highest accuracy is 99.21 % when the Hurst parameter and second-order renyi entropy features are combined. Experimental results show that the vocal fold excitation signal can express the pathological information about sound efficiently, which can be used for the automatic detection and classification of the pathological voice." @default.
• W2896755317 created "2018-10-26" @default.
• W2896755317 creator A5016058045 @default.
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• W2896755317 date "2018-06-01" @default.
• W2896755317 modified "2023-09-26" @default.
• W2896755317 title "Voice Pathology Detection Based on the Vocal Fold Signal and the Vocal Tract Signal Separation" @default.
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• W2896755317 doi "https://doi.org/10.1109/icivc.2018.8492760" @default.
• W2896755317 hasPublicationYear "2018" @default.
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