Speech Enhancement Using EMD Based Adaptive Soft-Thresholding (EMD-ADT)

This paper presents a novel algorithm of speech enhancement using data adaptive softthresolding technique. The noisy speech signal is decomposed into a finite set of band limited signals called intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Each IMF is divided into fixed length subframes. On the basis of noise contamination, the subframes are classified into two groups – noise dominant and speech dominant. Only the noise dominant subframes are thresholded for noise suppression. A data adaptive threshold function is computed for individual IMF on the basis of its variance. We propose a function for optimum adaptation factor for adaptive thresholding which was previously prepared by the least squares method using the estimated input signal to noise ratio (SNR) and calculated adaptation factor to obtain maximum output SNR. Moreover, good efficiency of the algorithm is achieved by an appropriate subframe processing. After noise suppression, all the IMFs (including the residue) are summed up to reconstruct the enhanced speech signal. The experimental results illustrate that the proposed algorithm show a noticeable efficiency compared to the recently developed speech denoising methods.