Mm - hmm.
And it seems to be the case. Um. Uh , the second thing is the  this spectral subtraction. Um. Um , which I 've just started yesterday to launch a bunch of , uh ,  twenty - five experiments , uh , with different , uh , values for the parameters that are used. So , it 's the Makhoul - type spectral subtraction which use  an over - estimation factor. So , we substr I subtract more ,   um ,   noise than the noise spectra that  is estimated  on the noise portion of the s uh , the utterances. So I tried several , uh , over - estimation factors. And after subtraction , I also add  a constant noise , and I also try different , uh ,  noise , uh , values and we 'll see what happen.
Hmm. OK.
Mm - hmm. Mm - hmm. But st still when we look at the , um  Well , it depends on the parameters that you use , but for moderate over - estimation factors and moderate noise level that you add , you st have a lot of musical noise. Um. On the other hand , when you  subtract more and when you add more noise , you get rid of this musical noise but  maybe you distort a lot of speech. So. Well. Mmm. Well , it  until now , it doesn't seem to help. But We 'll see. So the next thing , maybe I  what I will  try to  to do is just  to try to smooth mmm ,  the , um  to smooth the d the result of the subtraction , to get rid of the musical noise , using some kind of filter , or