ashudva/DNS-Challenge

This repo contains the scripts, models and required files for the Interspeech 2020 Deep Noise Suppression (DNS) Challenge. We are open sourcing clean speech and noise files as well. Participants of this challenge will use the scripts from this repo to create data to train their noise suppressors. They will compare their method with our baseline noise suppressor and report the results.

Deep Noise Suppression (DNS) Challenge - Interspeech 2020

This repository contains the datasets and scripts required for the DNS challenge. For more details about the challenge, please visit https://dns-challenge.azurewebsites.net/ and refer to our paper.

Repo details:

• The datasets directory contains the clean speech and noise clips.
• The NSNet-baseline directory contains the inference scripts and the ONNX model for the baseline Speech Enhancer called Noise Suppression Net (NSNet)
• noisyspeech_synthesizer_singleprocess.py - is used to synthesize noisy-clean speech pairs for training purposes.
• noisyspeech_synthesizer.cfg - is the configuration file used to synthesize the data. Users are required to accurately specify different parameters.
• audiolib.py - contains modules required to synthesize datasets
• utils.py - contains some utility functions required to synthesize the data
• unit_tests_synthesizer.py - contains the unit tests to ensure sanity of the data

Prerequisites

• Python 3.0 and above
• Soundfile (pip install pysoundfile), librosa

Usage:

1. Install librosa

pip install librosa

2. Install Git Large File Storage for faster download of the datasets.

git lfs install
git lfs track "*.wav"
git add .gitattributes

3. Clone the repository.

git clone https://github.com/microsoft/DNS-Challenge DNS-Challenge

4. Edit noisyspeech_synthesizer.cfg to include the paths to clean speech and noise directories. Also, specify the paths to the destination directories and store logs.
5. Create dataset

python noisyspeech_synthesizer_multiprocessing.py

Citation:

For the datasets and the DNS challenge:

@article{reddy2020interspeech,
  title={The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results},
  author={Reddy, Chandan KA and Gopal, Vishak and Cutler, Ross and Beyrami, Ebrahim and Cheng, Roger and Dubey, Harishchandra and Matusevych, Sergiy and Aichner, Robert and Aazami, Ashkan and Braun, Sebastian and others},
  journal={arXiv preprint arXiv:2005.13981},
  year={2020}
}

The baseline NSNet noise suppression:

@INPROCEEDINGS{9054254, author={Y. {Xia} and S. {Braun} and C. K. A. {Reddy} 
and H. {Dubey} and R. {Cutler} and I. {Tashev}}, 
booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, 
Speech and Signal Processing (ICASSP)}, 
title={Weighted Speech Distortion Losses for Neural-Network-Based Real-Time Speech Enhancement}, 
year={2020}, volume={}, number={}, pages={871-875},}

Contributing

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Legal Notices

Microsoft and any contributors grant you a license to the Microsoft documentation and other content in this repository under the Creative Commons Attribution 4.0 International Public License, see the LICENSE file, and grant you a license to any code in the repository under the MIT License, see the LICENSE-CODE file.

Microsoft, Windows, Microsoft Azure and/or other Microsoft products and services referenced in the documentation may be either trademarks or registered trademarks of Microsoft in the United States and/or other countries. The licenses for this project do not grant you rights to use any Microsoft names, logos, or trademarks. Microsoft's general trademark guidelines can be found at http://go.microsoft.com/fwlink/?LinkID=254653.

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Microsoft and any contributors reserve all other rights, whether under their respective copyrights, patents, or trademarks, whether by implication, estoppel or otherwise.

Dataset licenses

MICROSOFT PROVIDES THE DATASETS ON AN "AS IS" BASIS. MICROSOFT MAKES NO WARRANTIES, EXPRESS OR IMPLIED, GUARANTEES OR CONDITIONS WITH RESPECT TO YOUR USE OF THE DATASETS. TO THE EXTENT PERMITTED UNDER YOUR LOCAL LAW, MICROSOFT DISCLAIMS ALL LIABILITY FOR ANY DAMAGES OR LOSSES, INLCUDING DIRECT, CONSEQUENTIAL, SPECIAL, INDIRECT, INCIDENTAL OR PUNITIVE, RESULTING FROM YOUR USE OF THE DATASETS.

The datasets are provided under the original terms that Microsoft received such datasets. See below for more information about each dataset.

The datasets used in this project are licensed as follows:

1. Clean speech:

• https://librivox.org/; License: https://librivox.org/pages/public-domain/
• PTDB-TUG: Pitch Tracking Database from Graz University of Technology https://www.spsc.tugraz.at/databases-and-tools/ptdb-tug-pitch-tracking-database-from-graz-university-of-technology.html; License: http://opendatacommons.org/licenses/odbl/1.0/
• Edinburgh 56 speaker dataset: https://datashare.is.ed.ac.uk/handle/10283/2791; License: https://datashare.is.ed.ac.uk/bitstream/handle/10283/2791/license_text?sequence=11&isAllowed=y

2. Noise:

• Audioset: https://research.google.com/audioset/index.html; License: https://creativecommons.org/licenses/by/4.0/
• Freesound: https://freesound.org/ Only files with CC0 licenses were selected; License: https://creativecommons.org/publicdomain/zero/1.0/
• Demand: https://zenodo.org/record/1227121#.XRKKxYhKiUk; License: https://creativecommons.org/licenses/by-sa/3.0/deed.en_CA

Code license

MIT License

Copyright (c) Microsoft Corporation.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE