PhD A: So what 's what 's happened ? I think I 've missed something .
Professor B: OK . So a week ago maybe you weren't around when when when Hynek and Guenther and I ?
PhD C: Hynek was here .
PhD A: Yeah . I didn't .
Professor B: Oh , OK . So Yeah , let 's summarize . Um And then if I summarize somebody can tell me if I 'm wrong , which will also be possibly helpful . What did I just press here ? I hope this is still working .
PhD E: p - p - p
Professor B: We , uh we looked at , {nonvocalsound} uh anyway we  after coming back from QualComm we had , you know , very strong feedback and , uh , I think it was Hynek and Guenter 's and my opinion also that , um , you know , we sort of spread out to look at a number of different ways of doing noise suppression . But given the limited time , uh , it was sort of time to choose one .
PhD A: Mm - hmm . Mmm .
Professor B: Uh , and so , uh , th the vector Taylor series hadn't really worked out that much . Uh , the subspace stuff , uh , had not been worked with so much . Um , so it sort of came down to spectral subtraction versus Wiener filtering .
PhD A: Hmm .
Professor B: Uh , we had a long discussion about how they were the same and how they were d uh , completely different .
PhD A: Mm - hmm .
Professor B: And , uh , I mean , fundamentally they 're the same sort of thing but the math is a little different so that there 's a a  there 's an exponent difference in the index you know , what 's the ideal filtering , and depending on how you construct the problem .
PhD A: Uh - huh .
Professor B: And , uh , I guess it 's sort you know , after after that meeting it sort of made more sense to me because um , if you 're dealing with power spectra then how are you gonna choose your error ? And typically you 'll do choose something like a variance . And so that means it 'll be something like the square of the power spectra . Whereas when you 're when you 're doing the the , uh , um , looking at it the other way , you 're gonna be dealing with signals
PhD C: Mm - hmm .
Professor B: and you 're gonna end up looking at power uh , noise power that you 're trying to reduce . And so , eh so there should be a difference of you know , conceptually of of , uh , a factor of two in the exponent .
PhD A: Mm - hmm .
Professor B: But there 're so many different little factors that you adjust in terms of of , uh , uh , over - subtraction and and and and and so forth , um , that arguably , you 're c and and and the choice of do you do you operate on the mel bands or do you operate on the FFT beforehand . There 're so many other choices to make that are are almost well , if not independent , certainly in addition to the choice of whether you , uh , do spectral subtraction or Wiener filtering , that , um , @ @ again we sort of felt the gang should just sort of figure out which it is they wanna do and then let 's pick it , go forward with it . So that 's that was that was last week . And  and , uh , we said , uh , take a week , go arm wrestle , you know ,
Grad D: Oh .
Professor B: figure it out . I mean , and th the joke there was that each of them had specialized in one of them .
PhD A: Oh , OK .
Professor B: And and so they so instead they went to Yosemite and bonded , and and they came out with a single single piece of software . So it 's another another victory for international collaboration . So .
PhD A: So so you guys have combined or you 're going to be combining the software ?
Professor B: Uh .
PhD C: Well , the piece of software has , like , plenty of options ,
PhD E: Oh boy .
PhD C: like you can parse command - line arguments . So depending on that , it it becomes either spectral subtraction or Wiener filtering .
PhD A: Oh , OK .
PhD C: So , ye
PhD A: They 're close enough .
Professor B: Well , that 's fine , but the thing is the important thing is that there is a piece of software that you that we all will be using now .
PhD C: Yeah . Yeah .
Professor B: Yes .
PhD C: There 's just one piece of software .
PhD E: Yeah .
Professor B: Yeah .
PhD E: I need to allow it to do everything and even more more than this .
PhD C: Right .
PhD E: Well , if we want to , like , optimize different parameters of PhD C: Parameters . Yeah .
Professor B: Sure .
PhD E: Yeah , we can do it later . But , still so , there will be a piece of software with ,  uh , will give this system , the fifty - three point sixty - six , by default and Professor B: Mm - hmm .
PhD A: How how is how good is that ?
PhD E: Mm - hmm .
PhD A: I I I don't have a sense of PhD E: It 's just one percent off of the best proposal .
PhD C: Best system .
PhD E: It 's between i we are second actually if we take this system .
PhD A: OK .
Professor B: Yeah .
PhD C: Yeah .
PhD E: Right ?
PhD A: Compared to the last evaluation numbers ? Yeah .
Professor B: But , uh w which we sort of were before
PhD C: Yeah .
PhD E: Mm - hmm . Yeah .
Professor B: but we were considerably far behind . And the thing is , this doesn't have neural net in yet for instance . You know ?
PhD E: Mm - hmm .
PhD A: Hmm .
Professor B: So it so , um , it 's it it 's not using our full bal bag of tricks , if you will .
PhD A: Mm - hmm .
Professor B: And , uh , and it it is , uh , very close in performance to the best thing that was there before . Uh , but , you know , looking at it another way , maybe more importantly , uh , we didn't have any explicit noise , uh , handling stationary dealing with e e we didn't explicitly have anything to deal with stationary noise .
PhD A: Mm - hmm .
Professor B: And now we do .
PhD A: So will the neural net operate on the output from either the Wiener filtering or the spectral subtraction ? Or will it operate on the original ?
Professor B: Well , so so so argu arguably , I mean , what we should do I mean , I gather you have it sounds like you have a few more days of of nailing things down with the software and so on . But and then but , um , arguably what we should do is , even though the software can do many things , we should for now pick a set of things , th these things I would guess , and not change that .
PhD E: Mm - hmm .
Professor B: And then focus on everything that 's left . And I think , you know , that our goal should be by next week , when Hynek comes back , uh , to uh , really just to have a firm path , uh , for the you know , for the time he 's gone , of of , uh , what things will be attacked . But I would I would I would thought think that what we would wanna do is not futz with this stuff for a while because what 'll happen is we 'll change many other things in the system ,
PhD A: Mm - hmm .
Professor B: and then we 'll probably wanna come back to this and possibly make some other choices . But , um .