This paper proposes a frequency domain diversity approach for two or more microphone signals, for example, for in-car applications. The microphones should be positioned separately to insure diverse signal conditions and incoherent recording of noise. This enables a better compromise for the microphone position with respect to different speaker sizes and noise sources. This work proposes a two-stage approach. In the first stage, the microphone signals are weighted with respect to their signal-to-noise ratio and then summed similar to maximum ratio combining. The combined signal is then used as a reference for a frequency domain least-mean-squares (LMS) filter for each input signal. The output SNR is significantly improved compared to coherencebased noise reduction systems, even if one microphone is heavily corrupted by noise.

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A multichannel noise reduction and equalization approach for distributed microphones is presented. The speech enhancement is based on a blind-matched filtering algorithm that combines the microphone signals such that the output SNR is maximized. The algorithm is developed for spatially uncorrelated but nonuniform noise fields, that is, the noise signals at the different microphones are uncorrelated, but the noise power spectral densities can vary. However, no assumptions on the array geometry are made. The proposed method will be compared to the speech distortion-weighted multichannel Wiener filter (SDW-MWF). Similar to the SDW-MWF, the new algorithm requires only estimates of the input signal to noise ratios and the input cross-correlations. Hence, no explicit channel knowledge is necessary. A new version of the SDW-MWF for spatially uncorrelated noise is developed which has a reduced computational complexity, because matrix inversions can be omitted. The presented blind-matched filtering approach is similar to this SDW-MWF for spatially uncorrelated noise but additionally achieves some improvements in the speech quality due to a partial equalization of the acoustic system.

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