The official repo: McNet: Fuse Multiple Cues for Multichannel Speech Enhancement accepted by ICASSP 2023 (https://arxiv.org/pdf/2211.08872.pdf). Examples can be found at https://audio.westlake.edu.cn/Research/McNet.htm.
Table 1. Performance of offline speech enhancement.* means scores are quoted from the original papers.
| Method | NB-PESQ | WB-PESQ | STOI | SDR |
|---|---|---|---|---|
| Noisy | 1.82 | 1.27 | 87.0 | 7.5 |
| MNMF Beamforming * [20] | - | - | 94.0 | 16.2 |
| Oracle MVDR | 2.49 | 1.94 | 97.0 | 17.3 |
| CA Dense U-net * [12] | - | 2.44 | - | 18.6 |
| Narrow-band Net [11] | 2.74 | 2.13 | 95.0 | 16.6 |
| FT-JNF [14] | 3.17 | 2.48 | 96.2 | 17.7 |
| McNet (prop.) | 3.38 | 2.73 | 97.6 | 19.6 |
Table 2. Performance of online speech enhancement.
| Method | NB-PESQ | WB-PESQ | STOI | SDR |
|---|---|---|---|---|
| Noisy | 1.82 | 1.27 | 87.0 | 7.5 |
| Narrow-band Net [11] | 2.70 | 2.15 | 94.7 | 16.0 |
| FT-JNF [14] | 2.80 | 2.23 | 95.4 | 16.9 |
| McNet (prop.) | 3.29 | 2.67 | 97.2 | 19.0 |
Reminder: This project is built on the pytorch-lightning package, in particular its command line interface (CLI). To understand the commands below and config file, you need to have some basic knowledge about the CLI in lightning.
Train:
python McNetCLI.py fit --config config\mc_net_online.yaml
Test:
python McNetCLI.py test --config config\mc_net_online.yaml
If you want to use our pretrained model,
python McNetCLI.py test --config config/mc_net_offline.yaml --trainer.gpus 0,1 --ckpt_path model_checkpoints/offline/epoch494_criteria18.78_sdr18.78.ckpt
3.24 Add predict module
python McNetCLI.py predict --config config/mc_net_offline.yaml --trainer.gpus 0,1 --ckpt_path model_checkpoints/offline/epoch494_criteria18.78_sdr18.78.ckpt