Conditional maximum a posteriori (MAP), minima controlled recursive averaging
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
In this letter, we propose a novel method to improve the minima controlled recursive averaging (MCRA) based on the second-order conditional maximum a posteriori (CMAP). From an investigation of the previous 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 a current observation. To avoid this phenomenon, we propose the MCRA approach incorporating the second-order CMAP 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 two frames. Experimental results show that the proposed MCRA technique based on second-order CMAP yields better results compared to the previous MCRA method in speech enhancement.