Papers › Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression
Dual-Signal Transformation LSTM Network for Real-Time Noise Suppression
Nils L. Westhausen, Bernd T. Meyer
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This paper introduces a dual-signal transformation LSTM network (DTLN) for real-time speech enhancement as part of the Deep Noise Suppression Challenge (DNS-Challenge). This approach combines a short-time Fourier transform (STFT) and a learned analysis and synthesis basis in a stacked-network approach with less than one million parameters. The model was trained on 500 h of noisy speech provided by the challenge organizers. The network is capable of real-time processing (one frame in, one frame out) and reaches competitive results. Combining these two types of signal transformations enables the DTLN to robustly extract information from magnitude spectra and incorporate phase information from the learned feature basis. The method shows state-of-the-art performance and outperforms the DNS-Challenge baseline by 0.24 points absolute in terms of the mean opinion score (MOS).
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Enhancement | Deep Noise Suppression (DNS) Challenge | DTLN | PESQ-NB | 3.04 | #32 of 36 | Archive leaderboard | report |
| Speech Enhancement | Deep Noise Suppression (DNS) Challenge | DTLN | SI-SDR-WB | 16.34 | #32 of 36 | Archive leaderboard | report |
| Speech Enhancement | WHAMR! | DTLN | PESQ | 2.23 | #2 of 4 | Archive leaderboard | report |
| Speech Enhancement | WHAMR! | DTLN | SI-SDR | 2.12 | #2 of 4 | Archive leaderboard | report |
| Speech Enhancement | WHAMR! | DTLN | ΔPESQ | 0.4 | #2 of 4 | 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.
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