Speech Enhancement Based on Minima Controlled Recursive Averaging Technique Incorporating Conditional MAP

In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) which is based on the conditional maximum a posteriori criterion, A crucial component of a practical speech enhancement system is the estimation of the noise power spectrum, One state-of-the-art approach is the minima controlled recursive averaging (MCRA) technique, The noise estimate in the MCRA technique is obtained by averaging past spectral power values based on a smoothing parameter that is adjusted by the signal presence probability in frequency subbands. We improve the MCRA using the speech presence probability which is the a posteriori probability conditioned on both the current observation the speech presence or absence of the previous frame, With the performance criteria of the ITU-T P, 862 perceptual evaluation of speech quality (PESQ) and subjective evaluation of speech quality, we show that the proposed algorithm yields better results compared to the conventional MCRA-based scheme.