An Optimum Microphone Array Post-Filter for Speech Applications

This paper proposes a post-filtering estimation scheme for multichannel noise reduction. The proposed method extends and improves the existing Zelinski's and, the most general and prominent, McCowan's post-filtering methods that use the auto- and cross-spectral densities of the multichannel input signals to estimate the transfer function of the Wiener post-filter. A major drawback of these two speech enhancement algorithms 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 deals with this problem and can be considered as an optimal post-filter that is appropriate for a wide variety of different noise fields. In experiments over real-noise multichannel recordings, the proposed technique is shown to obtain a significant headstart over the other methods in terms of signal-to-noise ratio and speech degradation measures. In addition it is used for ASR experiments where promising preliminary results are presented.