  Most deep learning-based multi-channel speech enhancement methods focus on
designing a set of beamforming coefficients to directly filter the low
signal-to-noise ratio signals received by microphones, which hinders the
performance of these approaches. To handle these problems, this paper designs a
causal neural beam filter that fully exploits the spatial-spectral information
in the beam domain. Specifically, multiple beams are designed to steer towards
all directions using a parameterized super-directive beamformer in the first
stage. After that, the neural spatial filter is learned by simultaneously
modeling the spatial and spectral discriminability of the speech and the
interference, so as to extract the desired speech coarsely in the second stage.
Finally, to further suppress the interference components especially at low
frequencies, a residual estimation module is adopted to refine the output of
the second stage. Experimental results demonstrate that the proposed approach
outperforms many state-of-the-art multi-channel methods on the generated
multi-channel speech dataset based on the DNS-Challenge dataset.
