Nonlinear analysis and classification of vocal disorders.
This paper suggests a way to investigate pathological voice signals from nonlinear time series analysis for clinical applications. Primarily, self similar characteristics of vocal signals have been obtained by means of a discrete wavelet analysis. Moreover, the approximate entropy of the signals has been calculated as tools for classification. Furthermore, fuzzy c-means clustering has been employed for voice signal classification. Fuzzy membership function has been proposed as a way of quantifying the amount of disorder. The results show that proposed feature vector and classification method are reliable for voice signal analysis and disorder measurement.
Aghazadeh
B S
BS
Department of Mechanical Engineering, Tehran University, Tehran, Iran. b.aghazadeh@me.ut.ac.ir
Khadivi
H
H
Nikkhah-Bahrami
M
M
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013018:Sound Spectrography / Q000379:methods*; D013068:Speech Production Measurement / Q000379:methods*; D014832:Voice Disorders / Q000175:diagnosis*