Speech segregation is an important task of auditory scene analysis (ASA), in which the speech of a certain speaker is separated from other interfering signals. Wang and Brown proposed a multistage neural model for speech segregation, the core of which is a two-layer oscillator network. In this paper, we extend their model by adding further processes based on psychoacoustic evidence to improve the performance. These processes include pitch tracking and grouping based on amplitude modulation (AM). Our model is systematically evaluated and compared with the Wang-Brown model, and it yields significantly better performance.
