https://github.com/naplab/DANet

Deep Attractor Network (DANet) for single-channel speech separation
https://github.com/naplab/DANet

Last synced: 14 days ago
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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 (about 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 (4 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

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• 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