Abstract
Underdetermined reverberant speech separation is a challenging problem in source separation that has received considerable attention in both computational auditory scene analysis (CASA) and blind source separation (BSS). Recent studies suggest that, in general, the performance of frequency domain BSS methods suffer from the permutation problem across frequencies which degrades in high reverberation, meanwhile, CASA methods perform less effectively for closely spaced sources. This paper presents a method to address these limitations, based on the combination of binaural and BSS cues for the automatic classification of time-frequency (T-F) units of the speech mixture spectrogram. By modeling the interaural phase difference, the interaural level difference and frequency-bin mixing vectors, we integrate the coherent information for each source within a probabilistic framework. The Expectation Maximization (EM) algorithm is then used iteratively to refine the soft assignment of T-F regions to sources and re-estimate their model parameters. The coherence between the left and right recordings is also calculated to model the precedence effect which is then incorporated to the algorithm to reduce the effect of reverberation. Binaural room impulse responses for 5 different rooms with various acoustic properties have been used to generate the source images and the mixtures. The proposed method compares favorably with state-of-the-art baseline algorithms by Mandel et al. and Sawada et al., in terms of signal-to-distortion ratio (SDR) of the separated source signals.