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sednn

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deep learning based speech enhancement using keras or pytorch, make it easy to use

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최종 버전 다운로드 (.zip)

- - how to use pesq.txt
  - pesq
  - readme.txt
  - TEST_DR1_MRJO0_SI1364.WAV
  - TEST_DR1_MRJO0_SI1364_dnnEnh.wav
- - _pesq_itu_results.txt
  - _pesq_results.txt
  - how to compile.txt
  - pesq
  - pesq_source_code.zip
  - readme.txt
  - TEST_DR1_MRJO0_SI1364.WAV
- config.py
- file_list.csv
- get_csv.py
- istft.py
- main_decode_sednn_keras.py
- main_train_sednn_keras_v2.py
- prepare_fea.py
- preprocessing.py
- readme.txt
- spectrogram_to_wave.py
- tr_norm.pickle
- wav_reconstruction.py
- readme.txt
- test3_ForestGump_logMMSE_enh.wav
- test3_ForestGump_noisy.wav
- test3_ForestGump_Proposed DNN_enh.wav
- - enhanced_log_sp.png
- - test_noise.csv
  - test_timit.csv
  - train_noise.csv
  - train_timit.csv
- config.py
- data_generator.py
- evaluate.py
- LICENSE.txt
- main_dnn.py
- prepare_data.py
- README.md
- requirements.txt
- runme.sh
- spectrogram_to_wave.py
- config.py
- crash.py
- evaluate.py
- pesq
- prepare_data.py
- Remarks
- runme.sh
- stft.py
- test9.py
- tmp01.py
- tmp01b.py
- tmp01c.py
- tmp01d.py
- tmp01e.py
- tmp01f.py
- tmp02.py
- tmp02b.py
- tmp03.py
- tmp03b.py
- tmp04.py
- tmp04b.py
README.md

// repository documentation

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# deep_learning_for_speech_enhancement_keras_python
deep learning based speech enhancement using keras python

# Authors: YONG XU & QIUQIANG KONG

# Goal:
Make the GPU-C++ code project convert to python code which is much easier for the community to follow and use. The training and decoding code will be unified into the python code. Keras will be used as the toolkit.

# Invitation:
I want to invite you to be one of the contributors of this project, please contact me if you have interest. yong.xu.ustc@gmail.com

My final goal is to build a universal & robust deep learning based speech enhancement front end. And aslo try to adapt it to really serve for the speech recognition back-end.

# Ref:
The original GPU-C++ code: https://github.com/yongxuUSTC/DNN-for-speech-enhancement

Please cite the following papers if you use this code:

[1] A Regression Approach to Speech Enhancement Based on Deep Neural Networks. 
Yong Xu, Jun Du,Li-Rong Dai and Chin-Hui Lee, IEEE/ACM Transactions on Audio,Speech, and Language Processing,P.7-19,Vol.23,No.1, 2015 (2018 IEEE SPS Best paper award, citations > 600)

[2] An Experimental Study on Speech Enhancement Based on Deep Neural Networks. 
Yong Xu, Jun Du, Li-Rong Dai and Chin-Hui Lee,IEEE signal processing letters, p. 65-68,vol.21,no. 1,January 2014 (citations > 550)

[3] Multi-Objective Learning and Mask-Based Post-Processing for Deep Neural Network Based Speech Enhancement, 
Yong Xu, Jun Du, Zhen Huang, Li-Rong Dai, Chin-Hui Lee, Interspeech2015

Some DNN based speech enhancemen demos:

http://staff.ustc.edu.cn/~jundu/The%20team/yongxu/demo/SE_DNN_taslp.html

http://staff.ustc.edu.cn/~jundu/The%20team/yongxu/demo/IS15.html