arXiv:2206.15423cs.SD
Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain
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Abstract
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.
The goal of Meta AI Research is to do open research, not necessarily with direct applications at the time we start it. Indeed, the architecture, or the lessons learnt working on it can become useful later for the company, for instance for remote presence with VR, to isolate the…
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