Nonlinear Spatial Filtering For Multichannel Speech Enhancement In Inhomogeneous Noise Fields

A common processing pipeline for multichannel speech enhancement is to combine a linear spatial filter with a single-channel postfilter. In fact, it can be shown that such a combination is optimal in the minimum mean square error (MMSE) sense if the noise follows a multivariate Gaussian distribution. However, for non-Gaussian noise, this serial concatenation is generally suboptimal and may thus also lead to suboptimal results. For instance, in our previous work, we showed that a joint spatial-spectral nonlinear estimator achieves a performance gain of 2.6 dB segmental signal-to-noise ratio (SNR) improvement for heavy-tailed large-kurtosis multivariate noise compared to the traditional combination of a linear spatial beamformer and a postfilter. In this paper, we show that a joint spatial-spectral nonlinear filter is not only advantageous for noise distributions that are significantly more heavy-tailed than a Gaussian but also for distributions that model inhomogeneous noise fields while having rather low kurtosis. In experiments with artificially created noise we measure a gain of 1 dB for inhomogenous noise with low kurtosis and up to 2 dB for inhomogeneous noise fields with moderate kurtosis.