So that was one of the  bugs that was there in estimating the noise. And , uh , so once it was cleared , uh , I ran a few experiments with  different ways of smoothing the estimated clean speech and how t estimated the noise and , eh , smoothing the SNR also. And so the  the trend seems to be like ,  uh , smoothing the  current estimate of the clean speech for deriving the SNR , which is like  deriving the Wiener filter , seems to be helping. Then updating it quite fast using a very small time constant. So we 'll have , like , a few results where the  estimating the  the  More smoothing is helping. But still it 's like  it 's still comparable to the baseline. I haven't got anything beyond the baseline. But that 's , like , not using any Wiener filter. And , uh , so I 'm  I 'm trying a few more experiments with different time constants for smoothing the noise spectrum , and smoothing the clean speech , and smoothing SNR. So there are three time constants that I have. So , I 'm just playing around. So , one is fixed in the line , like  Smoothing the clean speech is  is helping , so I 'm not going to change it that much. But , the way I 'm estimating the noise and the way I 'm estimating the SNR , I 'm just trying  trying a little bit. So , that h And the other thing is , like , putting a floor on the , uh , SNR , because that  if some  In some cases the clean speech is , like  when it 's estimated , it goes to very low values , so the SNR is , like , very low. And so that actually creates a lot of variance in the low - energy region of the speech. So , I 'm thinking of , like , putting a floor also for the SNR so that it doesn't  vary a lot in the low - energy regions. And , uh. So. The results are , like  So far I 've been testing only with the  baseline , which is  which doesn't have any LDA filtering and on - line normalization. I just want to separate the  the contributions out. So it 's just VAD , plus the Wiener filter , plus the baseline system , which is , uh , just the spectral  I mean , the mel sp mel , uh , frequency coefficients. Um. And the other thing that I tried was  but I just  took of those , uh ,   Carlos filters , which Hynek had , to see whether it really h helps or not. I mean , it was just a  a run to see whether it really degrades or it helps. And it 's  it seems to be like it 's not  hurting a lot by just blindly picking up one filter which is nothing but a  four hertz  a band - pass m m filter on the cubic root of the power spectrum. So , that was the filter that Hy - uh , Carlos had. And so  Yeah. Just  just to see whether it really  it 's  it 's  is it worth trying or not. So , it doesn't seems to be degrading a lot on that. So there must be something that I can  that can be done with that type of noise compensation also , which   I guess I would ask Carlos about that. I mean , how  how he derived those filters and  and where d if he has any filters which are derived on OGI stories , added with some type of noise which  what we are using currently , or something like that. So maybe I 'll
This is cubic root of power spectra ?
Yeah. Cubic root of power spectrum.
So , if you have this band - pass filter , you probably get n you get negative values. Right ?