Wavelet based speech signal de-noising using hybrid thresholding

The wavelet transform has become a powerful tool of signal analysis and is widely used in many applications including signal detection and de-noising. Wavelet thresholding de-noising techniques provide a new way to reduce background noise in speech signal. However, the soft thresholding is best in reducing noise but worst in preserving edges, and hard thresholding is best in preserving edges but worst in de-noising. Motivated by finding a more general case that incorporates the soft and hard thresholding to achieve a compromise between the two methods, the hybrid thresholding method is proposed in this paper for noisy speech co-efficient to reduce the noise. To evaluate the performance of the proposed method a clean speech data set from the TIMIT database with white noise for SNR levels ranging from -10 db to +10 db. Finally, the experimental results show that the proposed hybrid thresholding is superior in speech signal denoising as compared to hard and soft thresholding methods.