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

2022-06-30 17:13:01
Dejan Markovic, Alexandre Defossez, Alexander Richard
arXiv_SD
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2206.15423

PDF

https://arxiv.org/pdf/2206.15423.pdf

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