Supervised speech enhancement using compressed sensing

Supervised approaches for speech enhancement require models to be learned for different noisy environments, which is a difficult criterion to meet in practical scenarios. In this paper, compressed sensing (CS) based supervised speech enhancement approach is proposed, where model (dictionary) for noise is derived from the noisy speech signal. It exploits the observation that unvoiced/silence regions of noisy speech signal will be predominantly noise and a method is proposed to measure the same, thus eliminating pre-training of noise model. The proposed method is particularly effective in scenarios where noise type is not known a priori. Experimental results validate that the proposed approach can be an alternative to the existing approaches for speech enhancement.