Separation and Dereverberation of Speech Signals with Multiple Microphones

Abstract

Speech enhancement was not and should not be examined solely with the tool of time-frequency analysis. Approaching this problem from different perspectives or incorporating other knowledges helps to expand the number of options open to us when developing a speech enhancement system. Using multiple microphones at different locations makes it possible to develop more sophisticated source separation and dereverberation technologies for speech enhancement, which enable man-made systems to extract a speech signal of interest in a noisy environment with competing speech and/or noise sources. This phenomenon is referred to as the cocktail party effect demonstrated by human beings and many other creatures with few efforts. However, separating and dereverberating speech signals is a very difficult problem in reverberant environments and the state-of-the-art algorithms are still unsatisfactory. The challenge lies in the coexistence of spatial interference from competing sources and temporal echoes due to room reverberation in the observed microphone signals. Focusing only on optimizing the signal-to-interference ratio is inadequate for most speech processing systems where source separation and speech dereverberation are two fully-integrated problems. In this chapter, we study these two problems in a unified framework. We deduce that spatial interference and temporal reverberation can be separated and a SIMO system with the speech signal of interest as input is extracted from the MIMO system. Furthermore, this interference-free SIMO system is dereverberated using the MINT theorem. Such a two-stage procedure leads to a novel sequential source separation and speech dereverberation algorithm based on blind multichannel identification. Simulations with measurements obtained in the varechoic chamber at Bell Labs verified the proposed algorithm.