But it was just a thought that  that it might be something to try.
Mm - hmm. Mm - hmm. Yep. Well , actually I tried ,  something else based on this , um , is to  to put some smoothing , um , because it seems to  to help or it seems to help the Wiener filtering
Mm - hmm.
and , mmm  So what I did is , uh , some kind of nonlinear smoothing. Actually I have a recursion that computes  Yeah , let me go back a little bit. Actually , when you do spectral subtraction you can , uh , find this  this equivalent in the s in the spectral domain. You can uh compute , y you can say that d your spectral subtraction is a filter , um , and the gain of this filter is the , um ,  signal energy minus what you subtract , divided by the signal energy. And this is a gain that varies over time , and , you know , of course , uh , depending on the s on the noise spectrum and on the speech spectrum. And  what happen actually is that during low SNR values , the gain is close to zero but it varies a lot. Mmm , and this  this is the cause of musical noise and all these  the   the fact you  we go below zero one frame and then you can have an energy that 's above zero.