  We present a single-stage casual waveform-to-waveform multichannel model that
can separate moving sound sources based on their broad spatial locations in a
dynamic acoustic scene. We divide the scene into two spatial regions
containing, respectively, the target and the interfering sound sources. The
model is trained end-to-end and performs spatial processing implicitly, without
any components based on traditional processing or use of hand-crafted spatial
features. We evaluate the proposed model on a real-world dataset and show that
the model matches the performance of an oracle beamformer followed by a
state-of-the-art single-channel enhancement network.
