A: uh but but uh I think it 's probably , on s from what Stephane is saying , it 's it 's unlikely to get sort of active participation from the two sides until after they 've B: Well I could at least B: Well , I 'm going to be out next week
B: but I could try to look into like this uh CVS over the web .
B: That seems to be a very popular way of people distributing changes and over , you know , multiple sites and things
A: Mm - hmm .
B: so maybe if I can figure out how do that easily and then pass the information on to everybody so that it 's you know , as easy to do as possible
B: and and people don't it won't interfere with <comment> their regular work ,
B: then maybe that would be good .
B: And I think we could use it for other things around here too .
B: So .
A: Good .
C: That 's cool .
C: And if you 're interested in using CVS , I 've set it up here ,
C: so .
B: Oh great .
B: OK .
C: um j
B: I used it a long time ago
B: but it 's been a while
B: so maybe I can ask you some questions .
C: Oh .
C: So . I 'll be away tomorrow and Monday but I 'll be back on Tuesday or Wednesday .
B: OK .
A: Yeah .
A: Dave , the other thing , actually , is is this business about this wave form .
A: Maybe you and I can talk a little bit at some point about coming up with a better uh demonstration of the effects of reverberation for our web page ,
A: cuz
A: uh  the uh um
A: I mean , actually the the
A: uh
A: It made a good good audio demonstration because when we could play that clip the the the really obvious difference is that you can hear two voices and   in the second one and only hear B: Maybe we could just like , talk into a cup .
A: Yeah .
B: Some good reverb .
A: No , I mean , it sound it sounds pretty reverberant ,
A: but I mean you can't when you play it back in a room with a you know a big room , nobody can hear that difference really .
C: Yeah .
C: Uh - huh .
A: They hear that it 's lower amplitude and they hear there 's a second voice ,
A: um but uh
A: that actually that makes for a perfectly good demo
A: because that 's a real obvious thing , that you hear two voices .
B: But not of reverberation .
A: Yeah .
C: A boom .
A: Well that that that 's OK .
A: But for the the visual , just , you know , I 'd like to have uh uh , you know , the spectrogram again ,
C: Yeah .
A: because you 're you 're you 're visual uh abilities as a human being are so good you can pick out A: you know , you you look at the good one , you look at the cru the screwed up one , and and you can see the features in it without trying to @ @ B: I noticed that in the pictures .
A: yeah .
B: I thought " hey , you know th " I B: My initial thought was " this is not too bad ! "
A: Right .
A: But you have to you know , if you look at it closely , you see " well , here 's a place where this one has a big formant A: uh uh formant maj major formants here are  are moving quite a bit . "
A: And then you look in the other one and they look practically flat .
B: Mm - hmm .
A: So I mean you could that 's why I was thinking , in a section like that , you could take a look look at just that part of the spectrogram and you could say " Oh yeah .
A: This this really distorted it quite a bit . "
B: Yeah .
B: The main thing that struck me in looking at those two spectrograms was the difference in the high frequencies .
B: It looked like for the one that was farther away , you know , it really everything was attenuated
B: and A: Right .
B: I mean that was the main visual thing that I noticed .
A: Right .
A: But it 's it 's
A: uh A: So .
A: Yeah .
A: So there are clearly are spectral effects .
A: Since you 're getting all this indirect energy , then a lot of it does have have uh reduced high frequencies .
A: But um the other thing is the temporal courses of things really are changed , and  and uh we want to show that , in some obvious way .
A: The reason I put the wave forms in there was because uh they they do look quite different .
A: Uh . And so I thought " Oh , this is good . "
A: but I  I just
A: uh A: After after uh they were put in there I didn't really look at them anymore ,
A: cuz I just they were different .
A: So I want something that has a is a more interesting explanation for why they 're different .
A: Um .
C: Oh .
C: So maybe we can just substitute one of these wave forms
C: and um then do some kind of zoom in on the spectrogram on an interesting area .
A: Something like that .
A: Yeah .
C: Uh - huh .
A: The other thing that we had in there that I didn't like was that um the most obvious characteristic of the difference uh when you listen to it is that there 's a second voice ,
A: and the the the the the uh cuts that we have there actually don't correspond to the full wave form .
A: It 's just the first A: I think there was something where he was having some trouble getting so much in ,
A: or .
A: I I forget the reason behind it .
A: But it it 's um  it 's the first six seconds or something of it
A: and it 's in the seventh or eighth second or something where @ @ the second voice comes in .
A: So we we would like to actually see the voice coming in , too , I think ,
C: Mm - hmm .
A: since that 's the most obvious thing when you listen to it .
A: So .
A: Um .
F: Uh , yeah .
F: Yeah .
F: I brought some I don't know if  some figures here .
F: Well . I start we started to work on spectral subtraction .
F: And um the preliminary results were very bad .
F: So the thing that we did is just to add spectral subtraction before this , the Wall uh process , which contains LDA on - line normalization .
A: Uh - huh .
F: And it hurts uh a lot .
A: Uh - huh .
F: And so we started to look at at um things like this ,
F: which is ,
F: well , it 's F: Yeah .
F: So you have the C - zero parameters for one uh Italian utterance .
D: You can @ @ .
F: And I plotted this for two channels .
F: Channel zero is the close mic microphone ,
F: and channel one is the distant microphone .
F: And it 's perfectly synchronized ,
F: so .
F: And the sentence contain only one word , which is " Due "
F: And it can't clearly be seen .
F: Where where is it ?
F: Where is the word ?
A: Uh - huh .
B: This is this is ,
E: Hmm .
B: oh , a plot of C - zero ,
F: So .
B: the energy .
F: This is a plot of C - zero , uh when we don't use spectral substraction ,
F: and when there is no on - line normalization .
F: So .
A: Mm - hmm .
F: There is just some filtering with the LDA
F: and and some downsampling , upsampling .
B: C - zero is the close talking ? F: So .
B: uh the close channel ?
F: Yeah . Yeah .
B: and s channel one is the F: Yeah .
F: So C - zero is very clean , actually .
B: Yeah .
F: Uh then when we apply mean normalization it looks like the second figure ,
F: though it is not .
F: Which is good .
F: Well , the noise part is around zero
F: and   And then the third figure is what happens when we apply mean normalization and variance normalization .
A: Mm - hmm .
F: So .
F: What we can clearly see is that on the speech portion the two channel come becomes very close ,
F: but also what happens on the noisy portion is that the variance of the noise is A: Mm - hmm .
B: This is still being a plot of C - zero ?
F: Yeah . This is still C - zero .
B: OK .
B: Can I ask um what does variance normalization do ?
B: w What is the effect of that ?
A: Normalizes the variance .
F: So it it F: Yeah .
F: It normalized th the standard deviation .
B: I mean
F: So it B: y Yeah .
F: You you get an estimate of the standard deviation .
B: No , I understand that ,
B: but I mean F: That 's
F: um B: No .
B: No , I understand what it is ,
B: but I mean , what does it what 's what is
F: Yeah but .
B: uh A: What 's the rationale ?
B: We
B: Yeah . Yeah .
B: Why why do it ?
F: Uh .
A: Well , I mean , because everything uh If you have a system based on Gaussians , everything is based on means and variances .
B: Yeah .
A: So if there 's an overall reason A: You know , it 's like uh if you were doing uh image processing
A: and in some of the pictures you were looking at , uh there was a lot of light
A: uh and and in some , there was low light ,
B: Mm - hmm .
A: you know , you would want to adjust for that in order to compare things .
B: Mm - hmm .
A: And the variance is just sort of like the next moment ,
A: you know ?
A: So uh what if um one set of pictures was taken uh so that throughout the course it was went through daylight and night uh um um ten times ,
A: another time it went thr
A: I mean i is , you know , how how much  how much vari
B: Oh , OK .
A: Or no .
A: I guess a better example would be how much of the light was coming in from outside
A: rather than artificial light .
A: So if it was a lot  if more was coming from outside , then there 'd be the bigger effect of the of the of the change in the