An EM Approach to Integrated Multichannel Speech Separation and Noise Suppression

In this contribution we provide a unified treatment of blind source separation (BSS) and noise suppression, two tasks which have traditionally been considered different and for which quite different techniques have been developed. Exploiting the sparseness of the sources in the short time frequency domain and using a probabilistic model which accounts for the presence of additive noise and which captures the spatial information of the multi-channel recording, a speech enhancement system is developed which suppresses noise and simultaneously separates speakers in case multiple speakers are active. Source activity estimation and model parameter estimation form the E-step and the M-step of the Expectation Maximization algorithm, respectively. Experimental results obtained on the dataset of the Signal Separation Evaluation Campaign 2010 demonstrate the effectiveness of the proposed system.