https://github.com/tencent-ailab/ultradualpathcompression

A Pytorch-based implementation of the compression and decompression module in "Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression".
https://github.com/tencent-ailab/ultradualpathcompression

Last synced: 2 days ago
JSON representation

A Pytorch-based implementation of the compression and decompression module in "Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression".

• Host: GitHub
• URL: https://github.com/tencent-ailab/ultradualpathcompression
• Owner: tencent-ailab
• License: apache-2.0
• Created: 2023-10-10T06:08:28.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-20T09:04:53.000Z (9 months ago)
• Last Synced: 2024-02-21T09:51:54.636Z (9 months ago)
• Language: Jupyter Notebook
• Size: 29.3 KB
• Stars: 23
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Ultra Dual-Path Compression and Decompression

This is the repository for a Pytorch-based implementation of the compression and decompression module in "Ultra Dual-Path Compression For Joint Echo Cancellation And Noise Suppression". The ultra dual-path compression module can compress the input multi-track spectra with large numbers of frames and frequency (T-F) bins into feature maps with small numbers of T-F bins, facilitating the fast processing for dual-path models (e.g., fullsubnet, 2D-convolution network). The decompression module transforms the compressed feature map back to the shapes of spectra for further processing. 

The latest codes are recommended to be found in `ultra_dual_path_compression.ipynb`, including dual-path compression, PostNet, an example of a dual-path GRU module, an example of a whole front-end network, and some examples of usage. Note that the codes of the dual-path GRU module and the whole front-end network are only for demonstration purposes and differ from what is in the article. Due to policy restrictions, the whole front-end network in the article will not be open-sourced at this time. `ultra_dual_path_compression.py` contains some legacy code, which will not be updated in the future.

Demos can be found in [DemoPage](https://hangtingchen.github.io/ultra_dual_path_compression.github.io/).

Please refer to our paper with the latest version on [Arxiv](https://arxiv.org/abs/2308.11053) for details. This paper is also accepted by [INTERSPEECH2023](https://www.isca-speech.org/archive/interspeech_2023/chen23t_interspeech.html).

Please cite the paper if you found this module useful.

```

@article{DBLP:journals/corr/abs-2308-11053,

  author       = {Hangting Chen and

                  Jianwei Yu and

                  Yi Luo and

                  Rongzhi Gu and

                  Weihua Li and

                  Zhuocheng Lu and

                  Chao Weng},

  title        = {Ultra Dual-Path Compression For Joint Echo Cancellation And Noise

                  Suppression},

  journal      = {CoRR},

  volume       = {abs/2308.11053},

  year         = {2023},

  url          = {https://doi.org/10.48550/arXiv.2308.11053},

  doi          = {10.48550/arXiv.2308.11053},

  eprinttype    = {arXiv},

  eprint       = {2308.11053},

  timestamp    = {Fri, 25 Aug 2023 12:09:57 +0200},

  biburl       = {https://dblp.org/rec/journals/corr/abs-2308-11053.bib},

  bibsource    = {dblp computer science bibliography, https://dblp.org}

}

```

## Disclaimer

This is not an officially supported Tencent product.