PhD E: No .
PhD D: Oh .
Professor C: And so i i his his He 's making the constraint it has to be at least two hundred milliseconds .
PhD D: Oh .
Professor C: And so you take that . And then he 's he 's uh measuring at the frame level still at the frame level , of what PhD D: Right .
Professor C: and then and then just uh normalizing with that larger amount . um and But one thing he was pointing out is when he he looked at a bunch of examples in log domain , it is actually pretty hard to see the change . And you can sort of see that , because of j of just putting it on the board that if you sort of have log - X plus log - X , that 's the log of X plus the log of two
PhD E: Yep .
PhD D: Yeah , maybe it 's not log distributed .
PhD E: Mmm . Yeah .
Professor C: and it 's just , you know , it it diminishes the effect of having two of them .
PhD E :
Professor C: Um .
PhD D: But you could do like a C D F there instead ? I mean , we don't know that the distribution here is normally .
Professor C: Yes , right . So So what I was suggesting to him is that PhD D: So just some kind of a simple Professor C: Actually , a PDF . But , you know , uh But , either way .
PhD D: PDF
Professor C: Yeah . Yeah , eith eith uh B
PhD D: Yeah .
PhD E: Yeah .
PhD D: Something like that where it 's sort of data driven .
Professor C: Yeah , but I think also u I think a good first indicator is when the the the researcher looks at examples of the data and can not see a change in how big the the signal is , when the two speaker PhD E: Yeah . Yeah .
Professor C: Then , that 's a problem right there . So . I think you should at least be able ,
PhD D: Oh yeah .
Professor C: doing casual looking and can get the sense , " Hey , there 's something there . " and then you can play around with the measures . And when he 's looking in the log domain he 's not really seeing it .
PhD D: Oh yeah .
PhD E: Yeah .
Professor C: So . And when he 's looking in straight energy he is , so that 's a good place to start .
PhD E: Yeah .
Professor C: Um . So that was that was the discussion we just had . Um . The other thing Actually we ca had a question for Adam in this . Uh , when you did the sampling ? uh over the speech segments or s or sampling over the the individual channels in order to do the e uh the amplitude equalization , did you do it over just the entire everything in the mike channels ?
PhD E: How Professor C: You didn't try to find speech ?
Grad A: No , I just took over the entire s uh entire channel um sampled ten minutes randomly .
Professor C: Right , OK . So then that means that someone who didn't speak very much would be largely represented by silence .
Grad A: Yep .
Professor C: And someone who would who would be So the normalization factor probably is i i i is is Grad A: Yeah , this was quite quick and dirty , and it was just for listening .
PhD E: Yeah .
Professor C: Yeah .
Grad A: And for listening it seems to work really well .
Professor C: OK .
PhD E: Yeah .
Professor C: Yeah .
PhD E: Yeah .
Grad A: So .
Professor C: Yeah . But that 's Grad A: But , it 's not Not a good measure .
Professor C: Right . So th
PhD E: Yeah .
Professor C: OK . So yeah there there there There 's a good chance then given that different people do talk different amounts that there is there there is still a lot more to be gained from gain norm normalization with some sort
PhD E: Yeah . Yeah . Mmm .
Grad A: Yes , absolutely .
Professor C: if if we can figure out a way to do it .
PhD E: Yeah .
Professor C: Uh . But we were agreed that in addition to that {comment} uh there should be s stuff related to pitch and harmonics and so forth .
PhD E: Yeah .
Professor C: So we didn't talk at all about uh the other derivatives , but uh again just just looking at Uh , I think uh Liz has a very good point , that in fact it would be much more graphic just to show Well , actually , you do have some distributions here , uh for these cases .
PhD E: Yeah . Yeah .
Professor C: You have some histograms , um and uh , they don't look very separate .
PhD E: Yeah .
Professor C: uh  separated .
PhD E: This is the the first derivate of log of frame energy uh without any kind of normalization .
PhD D: What Professor C: Yeah . Yeah . Yeah .
PhD D: Log energy . Sorry .
PhD E: These the These are the the first experiments uh with comment uh
PhD D: Frame energy .
Grad A: Except that it 's hard to judge this because the they 're not normalized . It 's just number of frames .
PhD D: Yeah .
Professor C: Yeah .
PhD D: Yeah .
PhD E: Yeah .
Grad A: But yeah , even so .
PhD D: W I mean , what I meant is , even if you use linear , you know , raw measures , like raw energy or whatever ,
Professor C: " Number " PhD D: maybe we shouldn't make any assumptions about the distribution 's shape , and just use you know , use the distribution to model the   the mean , or what y you know , rather than the mean take some Professor C: Yeah . But And so in in these he 's got that .
PhD D: Yeah .
Professor C: He 's got some pictures . But he doesn't he doesn't in the he i
PhD E: Yeah .
Professor C: just in derivatives , but not in the PhD D: Yeah . Oh .
Professor C: but he d but he doesn't doesn't PhD D: Right . So , we don't know what they look like on the , tsk {comment} For the raw .
Professor C: But he didn't h have it for the energy . He had it for the derivatives . Yeah .
PhD D: Yeah . So . I mean , there might be something there . I don't know .
Professor C: Yeah .
PhD D: Huh .
Grad A: Interesting
PhD E: Here I I
Professor C: Oh that yeah that 's a good q
PhD E: in No I I I haven't the result
Professor C: did did you have this sort of thing , for just the just the l r uh the the unnormalized log energy ? OK . Yeah . So she she 's right .
PhD E: but it 's the it 's the the the following .
Professor C: That 's a PhD D: Well it might be just good to know what it looks like .
Professor C: Yeah . That 's That 's uh cuz I 'd mentioned scatter plots before but she 's right ,
PhD D: Cuz PhD E: Huh ?
Professor C: I mean , even before you get the scatter plots , just looking at a single feature uh , looking at the distribution , is a good thing to do .
PhD E: Yeah . Catal - uh Combining the different possibilities of uh the parameters . I I I I mean the the the scatter plot combining eh different n two combination .
Professor C: Yeah , but but what she 's saying is , which is right , is le
PhD E: combination of two , of energy and derivate Professor C: I mean , let 's start with the Before we get complicated , let 's start with the most basic wh thing , which is we 're arguing that if you take energy uh if you look at the energy , that , when two people are speaking at the same time , usually  there 'll be more energy than when one is right ?
PhD E: Yeah .