Improved minima controlled recursive averaging technique using conditional maximum a posteriori criterion for speech enhancement

Abstract

In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) based on a conditional maximum a posteriori (MAP) criterion. From an investigation of the MCRA scheme, it is discovered that the MCRA method cannot take full consideration of the inter-frame correlation of voice activity since the noise power estimate is adjusted by the speech presence probability depending on an observation of the current frame. To avoid this phenomenon, the proposed MCRA approach incorporates the conditional MAP criterion in which the noise power estimate is obtained using the speech presence probability conditioned on both the current observation and the speech activity decision in the previous frame. Experimental results show that compared to the conventional MCRA method the proposed MCRA technique based on conditional MAP obtains low estimation error and when integrated into a speech enhancement system achieves improved speech quality.