PhD G: OK . So , uh u th I 've been playing with this Wiener filter , like . And there are there were some bugs in the program , so I was p initially trying to clear them up . Because one of the bug was I was assuming that always the VAD uh , the initial frames were silence . It always started in the silence state , but it wasn't for some utterances . So the it wasn't estimating the noise initially , and then it never estimated , because I assumed that it was always silence .
PhD C: Mm - hmm . So this is on SpeechDat - Car Italian ?
PhD G: Yeah .
PhD C: So , in some cases s there are also PhD G: SpeechDat - Car Italian . Yeah . There 're a few cases , actually , which I found later , that there are .
PhD C: o Uh - huh .
PhD G: 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 Professor B: This is cubic root of power spectra ?
PhD G: Yeah . Cubic root of power spectrum .
Professor B: So , if you have this band - pass filter , you probably get n you get negative values . Right ?
PhD G: Yeah . And I 'm , like , floating it to z zeros right now .
Professor B: OK .
PhD G: So it has , like the spectrogram has , like Uh , it actually , uh , enhances the onset and offset of I mean , the the begin and the end of the speech . So it 's there seems to be , like , deep valleys in the begin and the end of , like , high - energy regions ,
Professor B: Mm - hmm .
PhD G: because the filter has , like , a sort of Mexican - hat type structure .
Professor B: Mm - hmm .
PhD G: So , those are the regions where there are , like when I look at the spectrogram , there are those deep valleys on the begin and the end of the speech . But the rest of it seems to be , like , pretty nice .
Professor B: Mm - hmm .
PhD G: So . That 's something I observe using that filter . And Yeah . There are a few very not a lot of because the filter doesn't have a really a deep negative portion , so that it 's not really creating a lot of negative values in the cubic root . So , I 'll I 'll s may continue with that for some w I 'll I 'll Maybe I 'll ask Carlos a little more about how to play with those filters , and but while making this Wiener filter better . So . Yeah . That that 's it , Morgan .
Professor B: Uh , last week you were also talking about building up the subspace stuff ?
PhD G: Yeah . I I I would actually m m didn't get enough time to work on the subspace last week . It was mostly about finding those bugs and
Professor B: OK .
PhD G: th you know , things , and I didn't work much on that .