Papers › Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation
Deformable Temporal Convolutional Networks for Monaural Noisy Reverberant Speech Separation
William Ravenscroft, Stefan Goetze, Thomas Hain
Speech separation models are used for isolating individual speakers in many speech processing applications. Deep learning models have been shown to lead to state-of-the-art (SOTA) results on a number of speech separation benchmarks. One such class of models known as temporal convolutional networks (TCNs) has shown promising results for speech separation tasks. A limitation of these models is that they have a fixed receptive field (RF). Recent research in speech dereverberation has shown that the optimal RF of a TCN varies with the reverberation characteristics of the speech signal. In this work deformable convolution is proposed as a solution to allow TCN models to have dynamic RFs that can adapt to various reverberation times for reverberant speech separation. The proposed models are capable of achieving an 11.1 dB average scale-invariant signalto-distortion ratio (SISDR) improvement over the input signal on the WHAMR benchmark. A relatively small deformable TCN model of 1.3M parameters is proposed which gives comparable separation performance to larger and more computationally complex models.
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 | WHAMR! | Deformable TCN + Dynamic Mixing | MACs (G) | 3.7 | #15 of 18 | Archive leaderboard | report |
| Speech Separation | WHAMR! | Deformable TCN + Dynamic Mixing | Number of parameters (M) | 3.6 | #15 of 18 | Archive leaderboard | report |
| Speech Separation | WHAMR! | Deformable TCN + Dynamic Mixing | SDRi | 10.3 | #15 of 18 | Archive leaderboard | report |
| Speech Separation | WHAMR! | Deformable TCN + Dynamic Mixing | SI-SDRi | 11.1 | #15 of 18 | Archive leaderboard | report |
| Speech Separation | WHAMR! | Deformable TCN + Shared Weights + Dynamic Mixing | MACs (G) | 3.7 | #17 of 18 | Archive leaderboard | report |
| Speech Separation | WHAMR! | Deformable TCN + Shared Weights + Dynamic Mixing | Number of parameters (M) | 1.3 | #17 of 18 | Archive leaderboard | report |
| Speech Separation | WHAMR! | Deformable TCN + Shared Weights + Dynamic Mixing | SDRi | 9.5 | #17 of 18 | Archive leaderboard | report |
| Speech Separation | WHAMR! | Deformable TCN + Shared Weights + Dynamic Mixing | SI-SDRi | 10.1 | #17 of 18 | Archive leaderboard | report |
| Speech Separation | WSJ0-2mix | Deformable TCN + Dynamic Mixing | MACs (G) | 3.7 | #32 of 40 | Archive leaderboard | report |
| Speech Separation | WSJ0-2mix | Deformable TCN + Dynamic Mixing | Number of parameters (M) | 3.6 | #32 of 40 | Archive leaderboard | report |
| Speech Separation | WSJ0-2mix | Deformable TCN + Dynamic Mixing | SDRi | 17.4 | #32 of 40 | Archive leaderboard | report |
| Speech Separation | WSJ0-2mix | Deformable TCN + Dynamic Mixing | SI-SDRi | 17.2 | #32 of 40 | Archive leaderboard | report |
| Speech Separation | WSJ0-2mix | Deformable TCN + Shared Weights + Dynamic Mixing | MACs (G) | 3.7 | #34 of 40 | Archive leaderboard | report |
| Speech Separation | WSJ0-2mix | Deformable TCN + Shared Weights + Dynamic Mixing | Number of parameters (M) | 1.3 | #34 of 40 | Archive leaderboard | report |
| Speech Separation | WSJ0-2mix | Deformable TCN + Shared Weights + Dynamic Mixing | SDRi | 16.3 | #34 of 40 | Archive leaderboard | report |
| Speech Separation | WSJ0-2mix | Deformable TCN + Shared Weights + Dynamic Mixing | SI-SDRi | 16.1 | #34 of 40 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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