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https://github.com/naplab/DANet
Deep Attractor Network (DANet) for single-channel speech separation
https://github.com/naplab/DANet
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Deep Attractor Network (DANet) for single-channel speech separation
- Host: GitHub
- URL: https://github.com/naplab/DANet
- Owner: naplab
- License: mit
- Created: 2018-09-18T21:26:22.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2018-10-01T21:21:50.000Z (about 6 years ago)
- Last Synced: 2024-08-02T07:14:11.831Z (5 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 10.7 KB
- Stars: 75
- Watchers: 6
- Forks: 18
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-speech-enhancement - [Code
README
# Deep Attractor Network (DANet) for single-channel speech separation
This repository provides the implementation of the Deep Attractor Network (DANet) for single-channel speech separation in Jupyter Notebook (.ipynb) format. DANet was introduced in the following papers:
Zhuo Chen, Yi Luo, and Nima Mesgarani, [Deep attractor network for single-microphone speaker separation](https://ieeexplore.ieee.org/abstract/document/7952155)
Yi Luo, Zhuo Chen, and Nima Mesgarani, [Speaker-independent speech separation with deep attractor network](https://ieeexplore.ieee.org/abstract/document/8264702)
Informations about the papers can also be found in [our lab website](http://naplab.ee.columbia.edu/danet.html).
## Citation
If you find the scripts helpful in your research, please consider citing:
@inproceedings{chen2017deep,
title={Deep attractor network for single-microphone speaker separation},
author={Chen, Zhuo and Luo, Yi and Mesgarani, Nima},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on},
pages={246--250},
year={2017},
organization={IEEE}
}@article{luo2018speaker,
title={Speaker-independent speech separation with deep attractor network},
author={Luo, Yi and Chen, Zhuo and Mesgarani, Nima},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
volume={26},
number={4},
pages={787--796},
year={2018},
publisher={IEEE}
}
### Requirements
- Python 3.6.4
- Pytorch 0.4.1
- h5py 2.7.1
- sklearn 0.19.1
- numpy 1.15.0
- librosa 0.6.0
- jupyter 1.0.0 or above
- notebook 5.4.0 or above