I mean , y actually , you make me think a  a very important point here is that , um ,  if we a again try to look at how is this different from what we 're already doing ,  uh , there 's a  a , uh   a nasty argument that could be made th that it 's  it 's not different at  at all , because , uh  if you ignore the  the selection part because we are going into a  a very powerful ,  uh , nonlinearity that , uh , in fact is combining over time and frequency , and is coming up with its own  you know , better than Gabor functions its , you know , neural net functions ,
Mm - hmm.
its    whatever it finds to be best.
Um , so you could argue that in fact it  But I  I don't actually believe that argument because I know that , um ,  you can , uh  computing features is useful , even though  in principle you haven't   added anything  in fact , you subtracted something , from the original waveform  You know , uh , if you 've  you 've processed it in some way you 've typically lost something  some information. And so ,  you 've lost information and yet it does better with   with features than it does with the waveform. So , uh , I  I know that i sometimes it 's useful to   to constrain things. So that 's  why it really seems like the constraint  in  in all this stuff it 's the constraints that are actually what matters. Because if it wasn't  the constraints that mattered , then we would 've completely solved this problem long ago , because long ago we already knew how to put waveforms into powerful statistical mechanisms. So.