Alright on Test um Test test test Guess me Yeah OK Ooh Thursday So two sheets of paper in front of us Yeah So are these This is the arm wrestling Uh Yeah Yeah we Almost a coalition actually We already made it into one Oh good Yeah Yeah Excellent the best thing So tell me about it So well spectral subtraction or Wiener filtering um depending on if we put if we square the transfer function or not Right And then with of the noise depending on the uh the SNR with smoothing along time um smoothing along frequency very simple smoothing things And um the best result is when we apply this procedure on FFT bins uh with a Wiener filter And there is no noise addition after after that OK good because difficult when we have to add noise to to to find the right level OK Are you looking at one in in particular So of these two the sheet that gives three point Um the second sheet is abo uh about the same the same um idea but working on mel bands and a spectral subtraction instead of Wiener filter and there is also a noise addition after uh cleaning up the mel bins Mmm Well the results are similar Yeah I mean actually uh very similar I mean if you look at databases uh the uh one that has the smallest smaller overall number is actually better on the Finnish and Spanish uh but it is uh worse worse on the uh Aurora I mean on the uh the TI in Yeah uh uh Um Mmm So it probably doesn't matter that much either way But um when you say u uh unified do you mean uh one piece of software now or So now we are yeah setting up the software Um it should be ready uh very soon Um and we happened I think missed something OK So a week ago maybe you weren't around when when when Hynek Hynek Guenther here and I Yeah I didn't Oh OK So Yeah summarize Um And then if I summarize somebody can tell me if wrong which will also be possibly helpful What did I just press here I hope this is still working We uh we looked at uh anyway we after coming back from QualComm we had you know very strong feedback and uh I think it was Hynek and 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 Mmm 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 Hmm Uh we had a long discussion about how they were the same and how they were d uh completely different And uh I mean fundamentally the same sort of thing but the math is a little different so that a a an exponent difference in the index you know the ideal filtering and depending on how you construct the problem And uh I guess sort you know after after that meeting it sort of made more sense to me because um if dealing with power spectra then how are you gonna choose your error And typically do choose something like a variance And so that means be something like the square of the power spectra Whereas when when doing the the uh um looking at it the other way gonna be dealing with signals and gonna end up looking at power uh noise power that 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 But so many different little factors that you adjust in terms of of uh uh and and and and and so forth um that arguably c and and and the choice of do you do you operate on the mel bands or do you operate on the FFT beforehand 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 pick it go forward with it So that was that was last week And and uh we said uh take a week go arm wrestle you know Oh figure it out I mean and th the joke there was that each of them had specialized in one of them And and so Oh OK so instead they went to Yosemite and bonded and and they came out with a single single piece of software So another another victory for international collaboration So Uh So so you guys have combined or going to be combining the software Oh the boy piece of software has like plenty of options like you can parse arguments So depending on that it it becomes either spectral subtraction Oh Wiener filtering OK Well ye close fine enough but the thing is the important thing is that there is a piece of software that you that we all will be using Yeah now Yes just one piece of software I need to allow it to do everything and even more more than this Well Right if we want to like optimize different parameters Yeah Sure Yeah we can do it later But still so there will be a piece of software with uh will give this system the point by default and How how is how good is that I I I don't have a sense of just one percent off of the Best best system between i we are second actually if we take this system Right Yeah OK Compared uh to the last evaluation numbers Yeah Yeah w which we sort of were before but we were considerably far behind And the thing is this doesn't have neural net in yet for instance You know Hmm it so um it not using our full bal bag of tricks if you will 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 And now we do So will the neural net operate on the output from either the Wiener filtering or the spectral subtraction Well Or so operate so so on 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