A generalized estimation approach for linear and nonlinear microphone array post-filters

Stamatios Lefkimmiatis, Petros Maragos

Abstract: This paper presents a robust and general method for estimating the transfer functions of microphone array post-filters, derived under various speech enhancement criteria. For the case of the mean square error (MSE) criterion, the proposed method is an improvement of the existing McCowan post-filter, which under the assumption of a known noise field coherence function uses the auto- and cross-spectral densities of the microphone array noisy inputs to estimate the Wiener post-filter transfer function. In contrast to McCowan post-filter, the proposed method takes into account the noise reduction performed by the minimum variance distortionless response (MVDR) beamformer and obtains a more accurate estimation of the noise spectral density. Furthermore, the proposed estimation approach is general and can be used for the derivation of both linear and nonlinear microphone array post-filters, according to the utilized enhancement criterion. In experiments with real noise multichannel recordings the proposed technique has shown to obtain a significant gain over the other studied methods in terms of five different objective speech quality measures.