scholarly journals Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles

2017 ◽  
Vol 7 (2) ◽  
pp. 178 ◽  
Author(s):  
Juan Noda ◽  
Carlos Travieso ◽  
David Sánchez-Rodríguez
Keyword(s):  
Automatic Classification ◽  
Mel Frequency Cepstral Coefficients ◽  
Cepstral Coefficients

scholarly journals Voice Disorder Classification Based on Multitaper Mel Frequency Cepstral Coefficients Features

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Ömer Eskidere ◽  
Ahmet Gürhanlı
Keyword(s):  
Voice Disorder ◽  
Mel Frequency Cepstral Coefficients ◽  
Essential Information ◽  
Audio Processing ◽  
Voice Signal ◽  
Window Technique ◽  
Normal Voice ◽  
Cepstral Coefficients ◽  
Better Than

The Mel Frequency Cepstral Coefficients (MFCCs) are widely used in order to extract essential information from a voice signal and became a popular feature extractor used in audio processing. However, MFCC features are usually calculated from a single window (taper) characterized by large variance. This study shows investigations on reducing variance for the classification of two different voice qualities (normal voice and disordered voice) using multitaper MFCC features. We also compare their performance by newly proposed windowing techniques and conventional single-taper technique. The results demonstrate that adapted weighted Thomson multitaper method could distinguish between normal voice and disordered voice better than the results done by the conventional single-taper (Hamming window) technique and two newly proposed windowing methods. The multitaper MFCC features may be helpful in identifying voices at risk for a real pathology that has to be proven later.

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