A probabilistic approach to acoustic echo clustering and suppression
Mehrez Souden, Jason Wung, Biing-Hwang Fred Juang · 2013
This paper introduces an approach to cluster and suppress acoustic echo signals in hands-free, full-duplex speech communication systems. We employ the instantaneous recursive estimate of the magnitude squared coherence (MSC) of the echo line signal and the microphone signal, and model it with a two-component Beta mixture distribution. Since we consider the case of multiple microphone pickup, we further integrate the normalized recording vector as location feature into the proposed approach to achieve reliable soft decisions on the echo presence. The location information has been widely used for clustering-based blind source separation, and can be modeled using a Watson mixture distribution. Simulation evaluations of the proposed method show that it can achieve significant echo suppression performance.