UCL Discovery

Abstract

Temporal extensions of both linear and nonlinear anti-Hebbian learning have been shown to be suited to the problem of blind separation of sources from their convolved mixtures. This paper presents a generalized form of anti-Hebbian learning for a partially connected recurrent network based on the maximum likelihood estimation principle. Inspired by features of the binaural unmasking effect the network and associated online adaptation are applied to the enhancement of speech, which is corrupted by interfering noise, competing speech and reverberation. Graded simulations based on speech corrupted with increasingly complex levels of reverberation are reported. It is shown that for high levels of reverberation the proposed method compares favorably with classical adaptive filter approaches to speech enhancement in real acoustic environments.