PhD B: OK . We 're on .
Grad E: Hello ?
Professor A: OK , so uh had some interesting mail from uh Dan Ellis . Actually , I think he he redirected it to everybody also so uh the PDA mikes uh have a big bunch of energy at at uh five hertz uh where this came up was that uh I was showing off these wave forms that we have on the web 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 .
Grad C: Huh .
Professor A: So . So . I  I have to be more careful about using that as a as a  as a good illustration , uh , in fact it 's not , of uh  of the effects of room reverberation . It is isn't a bad illustration of the effects of uh room noise . on on uh some mikes uh but So . And then we had this other discussion about um whether this affects the dynamic range , cuz I know , although we start off with thirty two bits , you end up with uh sixteen bits and you know , are we getting hurt there ? But uh Dan is pretty confident that we 're not , that that quantization error is not is still not a significant factor there . So . So there was a question of whether we should change things here , whether we should change a capacitor on the input box for that or whether we should
PhD B: Yeah , he suggested a smaller capacitor , right ?
Professor A: Right . But then I had some other uh thing discussions with him
PhD B: For the P D
Professor A: and the feeling was once we start monk monkeying with that , uh , many other problems could ha happen . And additionally we we already have a lot of data that 's been collected with that , so .
PhD B: Yeah .
Professor A: A simple thing to do is he he he has a I forget if it this was in that mail or in the following mail , but he has a a simple filter , a digital filter that he suggested . We just run over the data before we deal with it .
PhD B: Mm - hmm .
Professor 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 . And I don't know if anybody knows .
Grad E: Um . I could go check .
Professor A: But . Yeah . So when we 're doing all these things using our software there is 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 and in general we like to do that , because of things like this and it 's it 's pretty it 's not a very severe filter . Doesn't affect speech frequencies , even pretty low speech frequencies , at all , but it 's
PhD B: What 's the cut - off frequency it used ?
Professor A: Oh . I don't know I wrote this a while ago
PhD B: Is it like twenty ?
Professor A: Something like that .
PhD B: Yeah .
Professor A: Yeah . I mean I think there 's some effect above twenty but it 's it 's it 's it 's mild . So , I mean it probably there 's probably some effect up to a hundred hertz or something but it 's it 's pretty mild . 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 PhD F: Uh . I think we use a pre - emphasis . Yeah . Yeah .
Professor 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 mike for . uh He says that there 's a pretty good roll off in the PZM mikes so we don't need need to worry about them one way or the other 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 . And then again if it 's 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 . uh But I also have to pick a different picture to show the effects of reverberation . uh
PhD B: Did somebody notice it during your talk ?
Professor A: uh No .
PhD B: Huh .
Professor A: Well . uh Well . If they made output they were they were , you know they were nice .
PhD B: Didn't say anything ?
Professor A: But . I mean the thing is it was since I was talking about reverberation and showing this thing that was noise , it wasn't a good match , but it certainly was still uh an indication of the fact that you get noise with distant mikes . 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 .
PhD B: Mm - hmm .
Professor A: So , I mean , it doesn't take deep  a new bold new methods to get rid of uh five hertz noise , so .
PhD B: Yeah .
Professor A: um uh But . So it was it was a bad example in that way , but it 's it still is it 's the real thing that we did get out of the microphone at distance , so it wasn't it w it w wasn't wrong it was inappropriate . So . So uh , but uh , Yeah , someone noticed it later pointed it out to me , and I went " oh , man . Why didn't I notice that ? "
PhD B: Hmm .
Professor A: um . So . um So I think we 'll change our our picture on the web , when we 're @ @ . 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 one was , that spectrogram that we show is O K , 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 you look at them it doesn't seem like it 's that bad uh because there 's many features that are still preserved . 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 , an and blow it up , and have that be the part that 's just to show as well . You know .
PhD B: Mm - hmm . Mm - hmm .
Professor A: i i Some things are going to be hurt . um Another , I was thinking of was um taking some spectral slices , like uh like we look at with the recognizer , and look at the spectrum or cepstrum that you get out of there , and the the uh , um , the reverberation uh does make it does change that . And so maybe maybe that would be more obvious .
PhD B: Hmm .
Grad C: Spectral slices ?
Professor A: Yeah .
Grad C: W w what d what do you mean ?
Professor A: Well , I mean um all the recognizers look at frames . So they they look at PhD B: So like one instant in time .
Professor A: Yeah , look at a Grad C: OK .
Professor A: So it 's , yeah , at one point in time or uh twenty over twenty milliseconds or something , you have a spectrum or a cepstrum .
Grad C: OK .
Professor A: That 's what I meant by a slice .
Grad C: I see .
Professor A: Yeah . And if you look at PhD B: You could just you could just throw up , you know , uh the uh some MFCC feature vectors . You know , one from one , one from the other , and then , you know , you can look and see how different the numbers are .
Professor A: Right . Well , that 's why I saying either  Well , either spectrum or cepstrum
PhD B: I 'm just kidding .
Professor A: but  but I think the thing is you wanna PhD B: I don't mean a graph . I mean the actual numbers .
Professor A: Oh . I see . Oh . That would be lovely , yeah .
PhD B: Yeah . " See how different these sequences of numbers are ? "
Professor A: Yeah . Or I could just add them up and get a different total .
PhD B: Yeah . It 's not the square .
Professor A: OK . Uh . What else wh what 's what else is going on ?