C: Uh - huh .
G: So that was one of the bugs that was there in estimating the noise .
G: 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 .
G: And so the the trend seems to be like , uh , smoothing the current estimate of the clean speech for deriving the SNR ,
G: which is like deriving the Wiener filter ,
G: seems to be helping . Then updating it quite fast
G: using a very small time constant .
G: So we 'll have , like , a few results where the G: estimating the G: the More smoothing is helping .
G: But still it 's like it 's still comparable to the baseline .
G: I haven't got anything beyond the baseline .
G: But that 's , like , not using any Wiener filter .
G: 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 .
G: So there are three time constants that I have .
G: So , I 'm just playing around .
G: So , one is fixed in the line , like Smoothing the clean speech is is helping ,
G: so I 'm not going to change it that much .
G: But , the way I 'm estimating the noise and the way I 'm estimating the SNR , I 'm just trying trying a little bit .
G: So , that h
G: And the other thing is , like , putting a floor on the , uh , SNR ,
G: because that if
G: some In some cases the clean speech is , like when it 's estimated , it goes to very low values ,
G: so the SNR is , like , very low .
G: And
G: so that actually creates a lot of variance in the low - energy region of the speech .
G: 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 .
G: And , uh . So . The results are , like G: So far I 've been testing only with the baseline , which is which doesn't have any LDA filtering and on - line normalization .
G: I just want to separate the the contributions out .
G: So it 's just VAD , plus the Wiener filter , plus the baseline system ,
G: which is , uh , just the spectral I mean , the mel sp mel , uh , frequency coefficients .
G: Um .
G: And the other thing that I tried was but I just took of those , uh ,  Carlos filters , which Hynek had ,
G: to see whether it really h helps or not .
G: I mean , it was just a a run to see whether it really degrades or it helps .
G: And
G: it 's it seems to be like it 's not hurting a lot by just blindly picking up one filter
G: which is nothing but a four hertz a band - pass m m filter on the cubic root of the power spectrum .
G: So , that was the filter that Hy - uh , Carlos had .
G: And
G: so G: Yeah . Just just to see whether it really it 's it 's is it worth trying or not .
G: So , it doesn't seems to be degrading a lot on that .
G: So there must be something that I can that can be done with that type of noise compensation also ,
G: which  I guess I would ask Carlos about that .
G: I mean , how how he derived those filters
G: and G: 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 ,
G: or something like that .
G: So maybe I 'll B: This is cubic root of power spectra ?
G: Yeah .
G: Cubic root of power spectrum .
B: So , if you have this band - pass filter , you probably get n you get negative values .
B: Right ?
G: Yeah .
G: And I 'm , like , floating it to z zeros right now .
B: OK .
G: So it has , like the spectrogram has , like G: Uh ,
G: it actually , uh , enhances the onset and offset of I mean , the the begin and the end of the speech .
G: So it 's there seems to be , like , deep valleys in the begin and the end of , like , high - energy regions ,
G: because the filter has , like , a sort of Mexican - hat type structure .
B: Mm - hmm .
G: So , those are the regions where there are , like B: Mm - hmm .
G: when I look at the spectrogram , there are those deep valleys on the begin and the end of the speech .
G: But the rest of it seems to be , like , pretty nice .
B: Mm - hmm .
G: So .
G: That 's something I observe using that filter .
G: And G: Yeah .
G: There are a few G: very G: not a lot of G: because the filter doesn't have a really a deep negative portion ,
G: so that it 's not really creating a lot of negative values in the cubic root .
G: So ,
G: I 'll I 'll s may continue with that for some w
G: I 'll I 'll Maybe I 'll ask Carlos a little more about how to play with those filters ,
G: and but while making this Wiener filter better .
G: So .
G: Yeah .
G: That that 's it , Morgan .
B: Uh ,
B: last week you were also talking about building up the subspace stuff ?
G: Yeah .
G: I I I would actually m m didn't get enough time to work on the subspace last week .
G: It was mostly about finding those bugs
G: and
G: th you know , things ,
B: OK .
G: and I didn't work much on that .
A: How about you , Carmen ?
D: Well , I am still working with , eh , VTS .
D: And , one of the things that last week ,
D: eh , say here is that maybe the problem was with the diff
D: because the signal have different level of energy .
B: Hmm ?
D: And , maybe , talking with Stephane and with Sunil , we decide that maybe it was interesting to to apply on - line normalization before applying VTS .
D: But then we decided that that 's it doesn't work absolutely , because we modified also the noise .
D: And D: Well , thinking about that , we we then we decide that maybe is a good idea .
D: We don't know .
D: I don't hav
D: I don't D: this is D: I didn't do the experiment yet to apply VTS in cepstral domain .
B: The other thing is B: So so , in i i
B: and B: Not B: and C - zero would be a different B: So you could do a different normalization for C - zero than for other things anyway .
B: I mean , the other thing I was gonna suggest is that you could have two kinds of normalization with with , uh , different time constants .
B: So ,
B: uh ,
B: you could do some normalization s uh , before the VTS ,
B: and then do some other normalization after .
B: I don't know .
B: But but C - zero certainly acts differently than the others do ,
D: Uh .
B: so that 's C: Mm - hmm .
D: Well , we s decide to m to to obtain the new expression if we work in the cepstral domain .
D: And D: Well . I am working in that now ,
B: Uh - huh .
D: but I 'm not sure if that will be usefu useful .
D: I don't know .
D: It 's k it 's k
D: It 's quite a lot It 's a lot of work .
D: Well , it 's not too much ,
B: Uh - huh .
D: but this it 's work .
D: And I want to know if if we have some feeling that the result B: Yeah .
D: I I would like to know if D: I don't have any feeling if this will work better than apply VTS aft in cepstral domain will work better than apply in m mel in filter bank domain .
D: I r I 'm not sure .
D: I don't I don't know absolutely nothing .
C: Mm - hmm .
B: Yeah . Well , you 're I think you 're the first one here to work with VTS ,
B: so ,
B: uh , maybe we could call someone else up who has ,
B: ask them their opinion .
B: Uh ,
C: Mm - hmm .
B: I don't I don't have a good feeling for it .
B: Um .
G: Pratibha .
C: Actually , the VTS that you tested before was in the log domain
C: and so the codebook is e e kind of dependent on the level of the speech signal .
D: Yeah ?
C: And C: So I expect it If if you have something that 's independent of this , I expect it to it to , uh , be a better model of speech .
D: To have better C: And .
C: Well .
B: You you wouldn't even need to switch to cepstra .
B: Right ?
B: I mean , you can just sort of normalize the C: No .
C: We could normali norm I mean , remove the median .
B: Yeah .
B: Yeah .
D: Mm - hmm .
B: And then you have one number which is very dependent on the level cuz it is the level ,
B: and the other which isn't .
C: Mm - hmm .
C: Yeah .
C: But here also we would have to be careful about removing the mean of speech
C: and
D: Ye
C: not of noise .
C: Because it 's like first doing general normalization
D: Yea
C: and then noise removal ,
C: which is D: Yeah .
D: We D: I was thinking to to to estimate the noise with the first frames
D: and then apply the VAD ,
B: Mm - hmm .
C: Mm - hmm .
D: before the on - line normalization .
D: We we see C: Mm - hmm .
D: Well ,
D: I am thinking about that and working about that ,
B: Yeah .
D: but I don't have result this week .
B: Sure .
B: I mean , one of the things we 've talked about maybe it might be star time to start thinking about pretty soon , is as we look at the pros and cons of these different methods , how do they fit in with one another ?
B: Because we 've talked about potentially doing some combination of a couple of them .
D: Mm - hmm .
B: Maybe maybe pretty soon we 'll have some sense of what their characteristics are ,
B: so we can see what should be combined .
C: Mm - hmm .
A: Is that it ?
B: OK .
A: OK ?
B: Why don't we read some digits ?
A: Yep .
A: Want to go ahead , Morgan ?
B: Sure .
A: Transcript L dash two one five .
B: O K .