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Found 2 papers, 1 papers with code
IIANet: An Intra- and Inter-Modality Attention Network for Audio-Visual Speech Separation
Recent research has made significant progress in designing fusion modules for audio-visual speech separation.
An efficient encoder-decoder architecture with top-down attention for speech separation
In addition, a large-size version of TDANet obtained SOTA results on three datasets, with MACs still only 10\% of Sepformer and the CPU inference time only 24\% of Sepformer.
Ranked #3 on
Speech Separation
on WHAM!
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