A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancement
2021-02-15Code Available1· sign in to hype
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ReproduceCode
- github.com/tvuong123/ModulationDomainLossOfficialpytorch★ 44
Abstract
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.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| Deep Noise Suppression (DNS) Challenge | RNN-Modulation | PESQ-WB | 2.75 | — | Unverified |
| DNS Challenge | RNN-Modulation | PESQ-WB | 2.75 | — | Unverified |
| VoiceBank + DEMAND | real-time-GRU | PESQ (wb) | 2.82 | — | Unverified |