Official codes for the article: A light-weight full-band speech enhancement model
DPARN is the acronym for dual-path attention -recurrent network.
We propose a spectral compression mapping (SCM) method to more effectively compress high-band spectral information, and utilize the multi-head attention mechanism to model the global spectral pattern.
Results for ablation study on self-built dataset:
| Models | PESQ | STOI | SI-SDR |
|---|---|---|---|
| Noisy | 1.45 | 0.90 | 5.00 |
| DPCRN | 2.03 | 0.88 | 8.93 |
| DPCRN-SCM | 2.48 | 0.92 | 11.57 |
| DPARN-SCM | 2.65 | 0.93 | 12.56 |
| DPRAN-SCM | 2.31 | 0.92 | 10.68 |
| DPAAN-SCM | 2.10 | 0.91 | 10.29 |
Comparison with other full-band/super-wide-band speech enhancement model on VCTK-DEMAND dataset:
| Models | Para. (M) | PESQ | STOI | SI-SDR |
|---|---|---|---|---|
| Noisy | - | 1.97 | 92.1 | 8.41 |
| RNNoise19 [2018] | 0.06 | 2.34 | - | - |
| PerceptNet3 [2020] | 8.0 | 2.73 | - | - |
| DeepFilterNet4 [2022] | 1.80 | 2.81 | - | 16.63 |
| S-DCCRN5 [2022] | 2.34 | 2.84 | 94.0 | - |
| DPARN [2022] | 0.89 | 2.92 | 94.2 | 18.28 |
soundfile: 0.10.3
librosa: 0.8.1
torch: 1.7.1
numpy: 1.20.3
scipy: 1.7.2
pandas: 1.3.4
tqdm: 4.62.3
- Use Dataset_split.py to split audios to equal-length segments.
- Use Training_csv.py to generate .csv file to pair noise and clean speech
- Use Dataloader.py to create dataset iterater
- Set parameters in Modules.py
- Use Network_Training.py starting training and save checkpoints
- Use Infer.py to enhance noisy speech based on trained checkpoint.