E: Hello ?
B: OK . We 're on .
A: OK , so uh had some interesting mail from uh Dan Ellis .
A: Actually , I think he he redirected it to everybody also
A: so uh the PDA mikes uh have a big bunch of energy at at uh five hertz
A: uh
A: where this came up was that uh I was showing off these wave forms that we have on the web
A: and and uh I just sort of hadn't noticed this , but that the major , major component in the wave in the second wave form in that pair of wave forms is actually the air conditioner .
C: Huh .
A: So .
A: So .
A: I  I have to be more careful about using that as a as a  as a good illustration ,
A: uh , in fact it 's not , of uh  of the effects of room reverberation .
A: It is isn't a bad illustration of the effects of uh room noise . on on uh some mikes
A: uh but
A: So .
A: And then we had this other discussion about um whether this affects the dynamic range ,
A: cuz I know , although we start off with thirty two bits , you end up with uh sixteen bits
A: and you know , are we getting hurt there ?
A: But uh Dan is pretty confident that we 're not , that that quantization error is not is still not a significant factor there .
A: So .
A: So there was a question of whether we should change things here ,
A: whether we should change a capacitor on the input box for that
A: or whether we should
B: Yeah , he suggested a smaller capacitor ,
B: right ?
B: For the P D
A: Right .
A: But then I had some other uh thing discussions with him
A: and the feeling was once we start monk monkeying with that , uh , many other problems could ha happen .
A: And additionally we we already have a lot of data that 's been collected with that ,
A: so .
B: Yeah .
A: A simple thing to do is
A: he he he has a I forget if it this was in that mail or in the following mail ,
A: but he has a a simple filter , a digital filter that he suggested .
A: We just run over the data before we deal with it .
B: Mm - hmm .
A: um The other thing that I don't know the answer to , but when people are using Feacalc here , uh whether they 're using it with the high - pass filter option or not .
A: And I don't know if anybody knows .
E: Um . I could go check .
A: But .
A: Yeah .
A: So when we 're doing all these things using our software there is A: um if it 's if it 's based on the RASTA - PLP program , which does both PLP and RASTA - PLP um then uh there is an option there which then comes up through to Feacalc which um allows you to do high - pass filtering
A: and in general we like to do that , because of things like this
A: and it 's it 's pretty it 's not a very severe
A: filter . Doesn't affect speech frequencies ,
A: even pretty low speech frequencies , at all ,
A: but it 's
B: What 's the cut - off frequency it used ?
A: Oh .
A: I don't know
A: I wrote this a while ago
B: Is it like twenty ?
A: Something like that .
B: Yeah .
A: Yeah .
A: I mean I think there 's some effect above twenty
A: but it 's it 's it 's it 's mild .
A: So , I mean it probably there 's probably some effect up to a hundred hertz or something but it 's it 's pretty mild .
A: I don't know in the in the STRUT implementation of the stuff is there a high - pass filter or a pre pre - emphasis or something in the F: Uh . I think we use a pre - emphasis .
F: Yeah .
F: Yeah .
A: So . We we we want to go and check that in i for anything that we 're going to use the P D
A: A mike for . uh He says that there 's a pretty good roll off in the PZM mikes
A: so we don't need need to worry about them one way or the other
A: but if we do make use of the cheap mikes , uh we want to be sure to do that that filtering before we process it .
A: And then again if it 's
A: uh depending on the option that the our our software is being run with , it 's it 's quite possible that 's already being taken care of .
A: uh But I also have to pick a different picture to show the effects of reverberation .
A: uh
B: Did somebody notice it during your talk ?
A: uh No .
B: Huh .
A: Well .
A: uh
A: Well . If they made output
A: they were they were , you know they were nice .
B: Didn't say anything ?
A: But . I mean the thing is it was
A: since I was talking about reverberation and showing this thing that was noise , it wasn't a good match ,
A: but it certainly was still uh an indication of the fact that you get noise with distant mikes .
B: Mm - hmm .
A: uh It 's just not a great example because not only isn't it reverberation but it 's a noise that we definitely know what to do .
A: So , I mean , it doesn't take deep  a new bold new methods to get rid of uh five hertz noise ,
A: so .
B: Yeah .
A: um uh But .
A: So it was it was a bad example in that way ,
A: but it 's it still is it 's the real thing that we did get out of the microphone at distance ,
A: so it wasn't it w it w wasn't wrong it was inappropriate .
A: So . So uh , but uh ,
A: Yeah , someone noticed it later pointed it out to me , and I went " oh , man .
A: Why didn't I notice that ? "
B: Hmm .
A: um . So . um
A: So I think we 'll change our our picture on the web , when we 're @ @ .
A: One of the things I was I mean , I was trying to think about what what 's the best way to show the difference an and I had a couple of thoughts
A: one was , that spectrogram that we show is O K ,
A: but the thing is the eyes uh and the the brain behind them are so good at picking out patterns from from noise that in first glance
A: you look at them it doesn't seem like it 's that bad
A: uh because there 's many features that are still preserved .
A: So one thing to do might be to just take a piece of the spec uh of the spectrogram where you can see that something looks different ,
A: an and blow it up , and have that be the part that 's just to show as well .
B: Mm - hmm . Mm - hmm .
A: You know .
A: i i Some things are going to be hurt .
A: um Another , I was thinking of was um taking some spectral slices ,
A: like uh like we look at with the recognizer ,
A: and look at the spectrum or cepstrum that you get out of there ,
A: and the the uh , um , the reverberation uh does make it does change that .
B: Hmm .
A: And so maybe maybe that would be more obvious .
C: Spectral slices ?
A: Yeah .
C: W w what d what do you mean ?
A: Well , I mean um all the recognizers look at frames .
B: So like one instant in time .
A: So they they look at C: OK .
A: Yeah , look at a A: So it 's ,
A: yeah ,
A: at one point in time or uh twenty over twenty milliseconds or something , you have a spectrum or a cepstrum .
C: OK .
A: That 's what I meant by a slice .
C: I see .
A: Yeah .
A: And if you look at B: You could just you could just throw up , you know , uh the uh some MFCC feature vectors .
B: You know , one from one , one from the other , and then , you know , you can look and see how different the numbers are .
A: Right . Well , that 's why I saying
B: I 'm just kidding .
A: either  Well , either spectrum or cepstrum
A: but  but I think the thing is you wanna B: I don't mean a graph .
B: I mean the actual numbers .
A: Oh . I see .
A: Oh .
A: That would be lovely ,
B: Yeah .
A: yeah .
B: " See how different these sequences of numbers are ? "
A: Yeah .
A: Or I could just add them up and get a different total .
B: Yeah .
B: It 's not the square .
A: OK .
A: Uh . What else wh what 's what else is going on ?
F: Uh , yeah .
F: Yeah , at first I had a remark
F: why I am wondering why the PDA is always so far .
F: I mean we are always meeting at the beginning of the table and the PDA 's there .
A: Uh . I guess cuz we haven't wanted to move it .
A: We we could  we could move us ,
F: Yeah ?
A: and .
F: OK .
E: That 's right .
F: Well , anyway .
F: Um .
F: Yeah , so .
F: Uh . Since the last meeting we 've we 've tried to put together um the clean low - pass um downsampling , upsampling , I mean ,
F: Uh the new filter that 's replacing the LDA filters ,
F: and also the um delay issue
F: so that F: We considered th the the delay issue on the for the on - line normalization .
F: Mmm .
F: So we 've put together all this
F: and then we have results that are not um  very impressive .
F: Well , there is no real improvement .