Speech Denoising in the Waveform Domain with Self-Attention
2022-02-15Code Available2· sign in to hype
Zhifeng Kong, Wei Ping, Ambrish Dantrey, Bryan Catanzaro
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ReproduceCode
- github.com/nvidia/cleanunetOfficialIn paperpytorch★ 347
Abstract
In this work, we present CleanUNet, a causal speech denoising model on the raw waveform. The proposed model is based on an encoder-decoder architecture combined with several self-attention blocks to refine its bottleneck representations, which is crucial to obtain good results. The model is optimized through a set of losses defined over both waveform and multi-resolution spectrograms. The proposed method outperforms the state-of-the-art models in terms of denoised speech quality from various objective and subjective evaluation metrics. We release our code and models at https://github.com/nvidia/cleanunet.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| Deep Noise Suppression (DNS) Challenge | CleanUNet | PESQ-WB | 3.15 | — | Unverified |