A New Robust Hybrid Approach to Enhance Speech in Mobile Communication Systems
Manimegalai Govindan Sumithra, Keppana Gounder Thanuskodi and Bharathi Deepa
DOI : 10.3844/ajassp.2011.332.342
American Journal of Applied Sciences
Volume 8, Issue 4
Problem statement: The received voice signal in mobile communication is often disturbed by background noise and hence there is a need for good noise reduction methods for enhancing Speech. It is well known that denoising is a compromise between the removal of the largest possible amount of noise and the preservation of signal integrity. To address this issue, a new method for enhancing speech from background interference is presented in this study by fusing dual band spectral subtraction with adaptive noise estimator and wavelet packet based thresholding method. Approach: The proposed system uses the combination of dual band Spectral Subtraction method with adaptive noise estimator for pre-processing, in order to initially reduce the noise level and further the quality of speech is improved by Wavelet Packet Transform (WPT) based level dependent thresholding method. The threshold value is determined by using Stein’s Unbiased Risk Estimator (SURE) and hard, soft, Garrotte, µ-law and a proposed modified soft thresholding functions are considered for denoising. Results: The proposed method was investigated by ten different clean speech samples (five male and five female) taken from TIMIT database and thirteen different noise sources to degrade the speech artificially and the energy level of the noise is scaled such that the overall SNR of the noisy speech is maintained at -5, 0,5,10 and 15 dB and finally the results are evaluated using objective and subjective measures. Conclusion/Recommendations: It is suggested from the experimental results that the proposed scheme gives improved spectral performance, reflects in better speech quality in all types of noisy environment. For better speech enhancement in noise dominated regions, the system efficiency is further improved by fusing threshold values for wavelet denoising.
© 2011 Manimegalai Govindan Sumithra, Keppana Gounder Thanuskodi and Bharathi Deepa. 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.