his ppt on icassp 2016
- python3
- pytorch
- kaldi
- matlab
- print (avg, max, median) of L2 norm
- gev BF
- add timer(of
- MVDR BF
- PESQ
- SNR
- F-score for each bin
cd your_kaldi/egs
git clone [email protected]:gogyzzz/heymann-nn-gev-bf.git
cd heymann-nn-gev-bf/s5
mylocal/prepare_noise.sh
mylocal/prepare_wsjcam0.sh
mylocal/prepare_mixed_wsjcam0.sh
matlab -nodesktop -nosplash -r \
"mix('ext/mixed/wsjcam0/si_dt/mixed.csv', 1024000); exit;"
mylocal/prepare_chime3.sh
# for pytorch dataset, dataloader
def wav_to_ibm(clean, noisy, channel=-1):
return (y_psd, x_psd, n_psd, x_mask, n_mask)
# psd normalization needed? -> no. just use batchnorm