Optimum post-filter estimation for noise reduction in multichannel speech processing

Stamatios Leukimmiatis, Petros Maragos

Abstract: This paper proposes a post-filtering estimation scheme for multichannel noise reduction. The proposed method is an extension and improvement of the existing Zelinski and McCowan post-filters which use the auto- and cross-spectral densities of the multichannel input signals to estimate the transfer function of the Wiener post-filter. A drawback in previous two post-filters is that the noise power spectrum at the beamformer's output is over-estimated and therefore the derived filters are sub-optimal in the Wiener sense. The proposed method overcomes this problem and can be used for the construction of an optimal post-filter which is also appropriate for a variety of different noise fields. 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 signal-to-noise ratio, log area ratio distance and speech degradation measure. In particular the proposed post-filter presents a relative SNR enhancement of 17.3% and a relative decrease on signal degradation of 21.7% compared to the best of all the other studied methods.