A multichannel MMSE-based framework for joint blind source separation and noise reduction
Mehrez Souden, Shoko Araki, Keisuke Kinoshita, Tomohiro Nakatani, Hiroshi Sawada · 2012
In this paper, we propose a new framework to separate multiple speech signals and reduce the additive acoustic noise using multiple microphones. In this framework, we start by formulating the minimum-mean-square error (MMSE) criterion to retrieve each of the desired speech signals from the observed mixtures of sounds and outline the importance of multi-speaker activity detection. The latter is modeled by introducing a latent variable whose posterior probability is computed via expectation maximization (EM) combining both the spatial and spectral cues of the multichannel speech observations. We experimentally demonstrate that the resulting joint blind source separation (BSS) and noise reduction solution performs remarkably well in reverberant and noisy environments.