https://github.com/ntia/wenets

Reference Implementations of Waveform Evaluation Networks (WEnets)
https://github.com/ntia/wenets

Last synced: 6 days ago
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Reference Implementations of Waveform Evaluation Networks (WEnets)

• Host: GitHub
• URL: https://github.com/ntia/wenets
• Owner: NTIA
• License: other
• Created: 2020-05-04T17:32:14.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-09-18T16:11:04.000Z (about 1 year ago)
• Last Synced: 2024-03-19T22:49:32.551Z (8 months ago)
• Language: Python
• Size: 48.6 MB
• Stars: 22
• Watchers: 3
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# WAWEnets Reference Implementations

MATLAB®/Octave and C++ implementations of Wideband Audio Waveform Evaluation networks or WAWEnets.

This WAWEnets implementation produces one or more speech quality or intelligibility values for each input speech signal without using reference speech signals.

WAWEnets are convolutional neural networks and they have been trained using full-reference objective speech quality and speech intelligibility values.

Details can be found in the WAWEnets paper.[1](#f1)

If you need to cite our work, please use the following:

```

@INPROCEEDINGS{

   9054204,

   author={A. A. {Catellier} and S. D. {Voran}},

   booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},

   title={Wawenets: A No-Reference Convolutional Waveform-Based Approach to Estimating Narrowband and Wideband Speech Quality},

   year={2020},

   volume={},

   number={},

   pages={331-335},

}

```

## MATLAB/Octave Implementation

Instructions for using the MATLAB/Octave implementation are found [here](matlab/readme.md).

## C++ Implementation

Instructions for building and using the C++ implementation are found [here](cplusplus/README.md).

## Python Implementation

Instructions for setting up and using the Python implementation are found [here](python/README.md)

-------------------------------------

1 Andrew A. Catellier & Stephen D. Voran, "WAWEnets: A No-Reference Convolutional Waveform-Based Approach to Estimating Narrowband and Wideband Speech Quality," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 331-335. [↩](#wawenets)