Multichannel speech reinforcement based on binaural unmasking

Abstract

Speech reinforcement or near-end listening enhancement is a technique that modifies the far-end signal to mitigate the effect of the near-end noise, usually based on the power spectra of the far-end signal and the near-end noise. Psychoacoustic experiments have shown that the location of a noise source with respect to that of a signal source affects the amount of masking. Since conventional speech reinforcement methods obtain spectral gain based only on the power spectra, this psychoacoustic phenomenon called binaural unmasking has not been considered in those approaches. In this paper, we propose a novel speech reinforcement algorithm that modifies the far-end speech signal based on both the power spectrum and the direction-of-arrival (DoA) of the noise. Specifically, we have computed the equivalent frontal noise level from the observed noise level and the estimated DoA, and used it to compute spectral gains as in conventional partial loudness restoration-based speech reinforcement. Experimental results showed that the proposed method outperformed the conventional methods based on partial loudness restoration and speech intelligibility index (SII) optimization in terms of the overall perceived quality through subjective listening tests.