ICASSP 2021: SDNet:Speaker and Direction Inferred Dual-channel Speech Separation

If you have the interest in our work, or use this code or part of it, please cite us!
Consider citing:

@inproceedings{li2021speaker,
  title={Speaker and Direction Inferred Dual-Channel Speech Separation},
  author={Li, Chenxing and Xu, Jiaming and Mesgarani, Nima and Xu, Bo},
  booktitle={ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={5779--5783},
  year={2021},
  organization={IEEE}
}

For more detailed descirption, you can further explore the whole paper with this link.

Requirements:

Pytorch>=1.1.0
resampy
soundfile

Model Descriptions:

Data Preparation

Please refer to predata_WSJ_lcx.py A more detailed dataset preparation procedure will be updated soon.

Train and Test

For train:
python train_WSJ0_SDNet.py

For test:
python test_WSJ0_SDNet.py

Please Modify the model path in test_WSJ0_SDNet.py.

Contact

If you have any questions please contact:
Email:lichenxing007@gmail.com

TODO

1. A brief implemention of SDNet
2. pretrained models.
3. separated samples.