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The official repo: "McNet: Fuse Multiple Cues for Multichannel Speech Enhancement", ICASSP 2023

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McNet

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

Train & Test

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

Update

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

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