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Speech Enhancement based on Minima Controlled Recursive Averaging Technique Incorporating Second-order Conditional Maximum a posteriori Criterion  

Kum, Jong-Mo (Department of Electronics Engineering, Inha University)
Chang, Joon-Hyuk (Department of Electronics Engineering, Inha University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.46, no.4, 2009 , pp. 132-138 More about this Journal
Abstract
In this paper, we propose a novel approach to improve the performance of minima controlled recursive averaging (MCRA) which is based on the second-order conditional maximum a posteriori (CMAP). 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 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 decisions in the previous two frames. Experimental results show that the proposed MCRA technique based on second-order conditional MAP yields better results compared to the conventional MCRA method.
Keywords
Speech Enhancement; Minima Controlled Recursive Averaging (MCRA); Second-order Conditional Maximum $\alpha$ posteriori (Second-order CMAP);
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