Enhanced speech separation in room acoustic environments with selected binaural cues

We propose a robust technique to separate audio sources received by a microphone array in a room acoustic environment with an underdetermined mixing process (i.e., the number of sources is larger than the number of mixtures). Our scheme consists of two stages: 1) estimation of mixing parameters and 2) recovery of source signals. For the first stage, contrary to the traditional DUET-type methods that exploit all binaural cues, we estimate the mixing parameters by selecting a reliable subset of binaural cues based on the phase determinacy condition and source sparsity. As a result, we can determine the mixing parameters successfully even in a reverberant environment with longer time delay. Then, proper mathematical tools are applied to the underdetermined linear system to recover the original audio sources for the second stage. Experimental results on simulated data in a room acoustic environment are given to show a significant gain over the DUET-type method in audio source separation.