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PESQ_dll
PESQ_dll
 
 
AMR-WB Baseline.ipynb
AMR-WB Baseline.ipynb
 
 
Analyze Model.ipynb
Analyze Model.ipynb
 
 
Evaluate Model.ipynb
Evaluate Model.ipynb
 
 
PESQ
PESQ
 
 
PESQ.so
PESQ.so
 
 
Prepare MTurk Data.ipynb
Prepare MTurk Data.ipynb
 
 
README.md
README.md
 
 
SA1.wav
SA1.wav
 
 
SX383.wav
SX383.wav
 
 
TIMIT Info.ipynb
TIMIT Info.ipynb
 
 
Train Model.ipynb
Train Model.ipynb
 
 
Visualizations.ipynb
Visualizations.ipynb
 
 
consts.py
consts.py
 
 
convert_TIMIT.py
convert_TIMIT.py
 
 
evaluation.py
evaluation.py
 
 
fiveYears.wav
fiveYears.wav
 
 
load_data.py
load_data.py
 
 
nn_blocks.py
nn_blocks.py
 
 
nn_util.py
nn_util.py
 
 
perceptual_loss.py
perceptual_loss.py
 
 
pesq.py
pesq.py
 
 
windowing.py
windowing.py
 
 

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End-to-End Optimized Speech Coding with Deep Neural Networks

Presented at ICASSP 2018.

Paper: https://arxiv.org/abs/1710.09064

Samples: http://srik.tk/speech-coding/

This code is very messy right now, and some work needs to be done to clean it up. More info about how to run it / reproduce results / etc. is coming soon. I believe all of the most important information (including hyperparameters) is in the paper, however.

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Code for "End-to-End Optimized Speech Coding with Deep Neural Networks" (ICASSP 2018)

srik.tk/speech-coding/

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  • Jupyter Notebook 89.3%
  • C 8.6%
  • Python 2.1%