Auditory Feature Extraction Based on Gammatone Filter Bank
Xiaoyu Cao · Jisuanji gongcheng · 2012
Aiming at the problem that speaker's feature coefficients have poor robustness in noise environment,this paper proposes an auditory cepstral coefficient for speaker recognition.It analyzes the working mechanism of the human auditory model,simulates the auditory model of human ear cochlea by Gammatone filter banks replaces the traditional triangular filter banks.Based on the nonlinear signal processing capability of human auditory model,exponential compression is used instead of the fixed logarithm compression.Simulation experiment is conducted based on Gaussian Mixed Model(GMM) recognition algorithm.Experimental results show that the auditory feature has better noise robustness than Mel Frequency Cepstral Coefficient(MFCC) and Linear Prediction Cepstral Coefficient(LPCC).