Using information theory to detect voice activity

Voice activity detection systems attempt to discriminate between voice and other ambient sounds. Most systems use a single microphone approach and rely on training prior to employment. The performance of these systems relies heavily on reverberation and noise levels. In this paper we present an unsupervised voice activity detection system that uses pairs of microphones to discern between a coherent acoustic source and spatially diffuse noise of low coherence. Measurement of coherency is performed using an information theoretic metric that integrates means to filter out more effectively the effect of reverberation and noise. Using extensive experiments, the performance of the system is investigated. Based on the conditions imposed by the experimental environments it is shown that the proposed system remains more robust than its counterparts in all cases.