Papers › Real-time Single-channel Dereverberation and Separation with Time-domainAudio...
Real-time Single-channel Dereverberation and Separation with Time-domainAudio Separation Network
Yi Luo, Nima Mesgarani
We investigate the recently proposed Time-domain Audio Sep-aration Network (TasNet) in the task of real-time single-channel speech dereverberation. Unlike systems that take time-frequency representation of the audio as input, TasNet learns anadaptive front-end in replacement of the time-frequency rep-resentation by a time-domain convolutional non-negative au-toencoder. We show that by formulating the dereverberationproblem as a denoising problem where the direct path is sepa-rated from the reverberations, a TasNet denoising autoencodercan outperform a deep LSTM baseline on log-power magnitudespectrogram input in both causal and non-causal settings. Wefurther show that adjusting the stride size in the convolutionalautoencoder helps both the dereverberation and separation per-formance.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Separation | WSJ0-2mix | TasNet v2 | SI-SDRi | 13.2 | #37 of 40 | Archive leaderboard | report |
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Methods
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