f Yeah. Mm - hmm. Yeah.
Yeah. OK. Yeah , but in this experiment I did  I didn't use any VAD. I just used the twenty first frame to estimate the noise. And  So I expected it to be a little bit better ,  if , uh , I use more  more frames. Um. OK , that 's it for spectral subtraction. The second thing I was working on is to , um , try to look at noise estimation ,  mmm , and using some technique that doesn't need voice activity detection. Um , and for this I u simply used some code that , uh ,  I had from  from Belgium , which is technique that , um , takes a bunch of frame , um , and for each frequency bands of this frame , takes a look at the minima of the energy. And then average these minima and take this as an  an energy estimate of the noise for this particular frequency band. And there is something more to this actually. What is done is that ,  uh , these minima are computed , um , based on , um , high resolution spectra. So , I compute an FFT based on the long , uh , signal frame which is sixty - four millisecond
So you have one minimum for each frequency ?
What  what I  what I d uh , I do actually , is to take a bunch of  to take a tile on the spectrogram and this tile is five hundred milliseconds long and two hundred hertz wide.