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IDEA

The code for performing dereverberation

Extracting log power spectrum: python build_data.py (Reverber_Inp) (LPS_Output)

python build_data.py ~/IDEA/train/3 ~/IDEA/Data/train/noisy_3 python build_data.py ~/IDEA/train/6 ~/IDEA/Data/train/noisy_6 python build_data.py ~/IDEA/train/9 ~/IDEA/Data/train/noisy_9 python build_data.py ~/IDEA/train/clean ~/IDEA/Data/train/clean

Training process:

EP stage:

python main.py --mode train --path 3/ --data 3 --gpus 0 python main.py --mode train --path 6/ --data 6 --gpus 0 python main.py --mode train --path 9/ --data 9 --gpus 0

EI stage:

python main.py --mode s_train --path soft/ --gpus 0

Testing process:

python main.py --mode s_test --path soft/ --data 3 --gpus 0 python main.py --mode s_test --path soft/ --data 6 --gpus 0 python main.py --mode s_test --path soft/ --data 9 --gpus 0 python main.py --mode s_test --path soft/ --data 4 --gpus 0 python main.py --mode s_test --path soft/ --data 7 --gpus 0 python main.py --mode s_test --path soft/ --data 10 --gpus 0

Reconstruction (from LPS to waveform)

python convert.py ~/IDEA/soft/3/ python convert.py ~/IDEA/soft/6/ python convert.py ~/IDEA/soft/9/ python convert.py ~/IDEA/soft/4/ python convert.py ~/IDEA/soft/7/ python convert.py ~/IDEA/soft/10/

Please cite the paper:

W. Lee, S. Wang, F. Chen, X. Lu, S. Chien and Y. Tsao, "Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm," in Proc. ICASSP, pp. 5454-5458, 2018

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