Papers › A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement
A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement
We describe a modulation-domain loss function for deep-learning-based speech enhancement systems. Learnable spectro-temporal receptive fields (STRFs) were adapted to optimize for a speaker identification task. The learned STRFs were then used to calculate a weighted mean-squared error (MSE) in the modulation domain for training a speech enhancement system. Experiments showed that adding the modulation-domain MSE to the MSE in the spectro-temporal domain substantially improved the objective prediction of speech quality and intelligibility for real-time speech enhancement systems without incurring additional computation during inference.
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 Enhancement | DNS Challenge | RNN-Modulation | PESQ-WB | 2.75 | #5 of 5 | Archive leaderboard | report |
| Speech Enhancement | Deep Noise Suppression (DNS) Challenge | RNN-Modulation | PESQ-WB | 2.75 | #24 of 36 | Archive leaderboard | report |
| Speech Enhancement | VoiceBank + DEMAND | real-time-GRU | PESQ (wb) | 2.82 | #41 of 42 | 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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