MHA-DPCRN

We design a spectral compression mapping (SCM) for full-band speech enhancement, and propose a two-stage stream named MHA-DPCRN

Rquirements

soundfile: 0.10.3
librosa: 0.8.1
torch: 3.7.10
numpy: 1.20.3
scipy: 1.7.2
pandas: 1.3.4
tqdm: 4.62.3

Usage

1. Use Dataset_split.py to split audios to equal-length segments.
2. Use Training_csv.py to generate .csv file to pair noise and clean speech
3. Use Dataloader.py to create dataset iterater
4. Set parameters in Modules.py
5. Use Network_Training.py starting training and save checkpoints
6. Use Infer.py to enhance noisy speech based on trained checkpoint.

URL

https://arxiv.org/ftp/arxiv/papers/2206/2206.13136.pdf