Yeah.
and on modeling Gaussians. And if you  Can I move a little bit with this ? Yeah. And if we introduce now this  this u spectral subtraction , or Wiener filtering stuff  So , usually what you have is maybe , um  I 'm  I 'm showing now an envelope um maybe you 'll  f for this time. So usually you have  maybe in clean condition you have something which looks like this. And if it is noisy it is somewhere here. And then you try to subtract it or Wiener filter or whatever. And what you get is you have always these problems , that you have this  these  these  these zeros in there.
Yeah.
And you have to do something if you get these negative values. I mean , this is your noise estimate and you somehow subtract it or do whatever. Uh , and then you have  And then I think what you do is you introduce some  some artificial distribution in this uh in  in the models. I mean , i you  you train it also this way but , i somehow there is  u u there is no longer a  a Gaussian distribution. It is somehow a strange distribution which we introduce with these  artificial distortions. And  and I was thinking that  that might be the reason why you get these problems in the  especially in the multi - condition training mode.