A Simplified Wiener Beamformer Based on Covariance Matrix Modelling

This paper is devoted to the problem of adaptive beamforming with small-spaced microphone arrays. In this context, the Wiener filter is an optimal beamformer in the mean-squared error (MSE) sense. However, it requires good estimates of the covariance matrices of the speech signal of interest and noise, which are difficult to achieve in time-varying and reverberant acoustic environments. To deal with this problem, we propose a general method by parametric modeling the covariance matrices of speech and noise, which leads to a simplified Wiener beamformer. This beamformer has only one time-varying parameter to estimate, which is much easier to achieve as compared to the estimation of covariance matrices. As an example, we adopt the parametric model used in the superdirective beamformer, which models the covariance matrices as a combination of the pseudo-coherence matrices of a point source and diffuse noise. Simulation results show that the developed beamformer outperforms the traditional Wiener beamformer in terms of both noise and reverberation suppression.