Sub band Speech analysis using Gammatone Filter banks and optimal pitch extraction methods for each sub band using average magnitude difference function (AMDF) for LPC Speech Coders in Noisy Environments

Abstract

Modern speech processing applications require operation on signal of interest that is contaminated by high level of noise. These situations call for a greater robustness in estimation of the speech parameters for mismatch environment and low environmental SNR level. In this paper the speech is analyzed with a Gammatone filter bank. This splits the full band speech signal s(n) into frequency bands(sub bands).and for each sub band speech signal pitch is extracted. We determine the Signal to Noise Ratio for each Sub band speech signal. Then the average of pitch periods of the highest SNR sub bands is used to obtain a optimal pitch value. This paper describes a computationally simple Pitch extraction algorithms using Average Magnitude Difference Function (AMDF) which is a new approach using weighted Autocorrelation 2 and very useful for accurate Pitch Period extraction. Both these algorithms can be software implemented and performance evaluated. Both of them uses center clipping for time domain processing. This paper also in general Compares the effectiveness of the new AMDF using weighted Autocorrelation and the existing Autocorrelation method and how it is possible to utilize this further in Speech Enhancement Systems using the proposed new algorithms for its implementation.