Binaural sound segregation for multisource reverberant environments

Abstract

We present a novel method for binaural sound segregation from acoustic mixtures contaminated by both multiple interference and reverberation. We employ the notion of an ideal time-frequency binary mask, which selects the target if it is stronger than the interference in a local time-frequency (T-F) unit. As opposed to classical adaptive filtering, which focuses on the suppression of noise, our model employs an adaptive filter that performs target cancellation. T-F units dominated by a target are largely suppressed at the output of the cancellation unit when compared to units dominated by noise. Consequently, the actual input-to-output attenuation level in each T-F unit is used to estimate an ideal binary mask. A systematic evaluation in terms of automatic speech recognition performance shows that the resulting system produces masks close to ideal binary ones.