Voice Activity Detection under Rayleigh distribution

Abstract

This paper presents an improved Voice Activity Detection (VAD) algorithm which uses the Signal-to-Noise Ratio (SNR) measure. We assume that noise Power Spectral Density (PSD) in each spectral bin follows a Rayleigh distribution. Rayleigh distributions with its asymmetric tail characteristics give a better description of the noise PSD distribution than Gaussian distribution. Under this assumption, a new threshold updating expression is derived. Since the analytical integral of the false alarm probability, the threshold updating expression can be represented without the inverse complementary error function and low computational complexity is achieved in our system. Experimental results show that the proposed VAD outperforms or at least is comparable with the VAD scheme presented by Davis under several noise environments and has a lower computational complexity.