Journal of Computer Science

Speech Enhancement Algorithm Using Sub band Two Step Decision Directed Approach with Adaptive Weighting factor and Noise Masking Threshold

Deepa Dhanaskodi and Shanmugam Arumugam

DOI : 10.3844/jcssp.2011.941.948

Journal of Computer Science

Volume 7, Issue 6

Pages 941-948

Abstract

Problem statement: Speech Enhancement plays an important role in any of the speech processing systems like speech recognition, mobile communication, hearing aid. Approach: In this work, human perceptual auditory masking effect is incorporated into the single channel speech enhancement algorithm. The algorithm is based on a criterion by which the audible noise may be masked rather than being attenuated and thereby reducing the chance of distortion to speech. The basic decision directed approach is for efficient reduction of musical noise, that includes the estimation of the a priori SNR which is a crucial parameter of the spectral gain, follows the a posteriori SNR with a delay of one frame in speech frames. In this work a simple adaptive speech enhancement technique, using an adaptive sigmoid type function to determine the weighting factor of the TSDD algorithm is employed based on a sub band approach. In turn the spectral estimate is used to obtain a perceptual gain factor. Results: Objective and subjective measures like SNR, MSE, IS distance and were obtained, which shows the ability of the proposed method for efficient enhancement of noisy speech Conclusion/Recommendations: Performance assessment shows that our proposal can achieve a more significant noise reduction and a better spectral estimation of weak speech spectral components from a noisy signal as compared to the conventional speech enhancement algorithm.

Copyright

© 2011 Deepa Dhanaskodi and Shanmugam Arumugam. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.