Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain

• URL: http://arxiv.org/abs/2206.15423v1
• Date: Thu, 30 Jun 2022 17:13:01 GMT
• Title: Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain
• Authors: Dejan Markovic, Alexandre Defossez, Alexander Richard
• Abstract summary: The model is trained end-to-end and performs spatial processing implicitly. We evaluate the proposed model on a real-world dataset and show that the model matches the performance of an oracle beamformer.
• Score: 131.74762114632404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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