sections Um uh if you if you did a filtering as Hirsch did in late eighties to reduce some of the effects of reverberation uh uh Avendano and Hermansky were arguing that uh perhaps one of the reasons for that working was ma may not have even been the filtering so much but the fact that when you filter a an power spectrum you get some negative values and you gotta figure out what to do with them if gonna continue treating this as a power spectrum So what what Hirsch did was uh set them to zero set the negative values to zero So if you imagine a a waveform all positive which is the time trajectory of energy um and uh shifting it downwards and then getting rid of the negative parts essentially throwing away the things And the parts of the speech where the reverberation is most audible You know you have the reverberation from things showing up in So in this case you have some artificially imposed thing I mean getting rid of some of the other effects of reverberation but because you have these windows getting these funny things coming in uh at n And um what if you did I mean nothing to say that the the processing for this has to be restricted to trying to get it back to the original according to some equation I mean you also could uh just try to make it nicer And one of the things you could do is you could do some sort of thing and you actually could take very sections and set them to some some uh very low or or near zero value I mean uh just saying if in fact it turns out that that these echoes that hearing are uh or whichever they are are are uh part of causing the problem you actually could get rid of them Be pretty simple OK I mean you do it in a pretty conservative way so that if you made a mistake you were more likely to keep in an echo than to throw out speech Hmm Um what is the reverberation time like there In thi in this room On uh the the one what the s in the speech that you are you are using like Y Yeah I I I I don't know So this room uh Oh this room this OK So so these are just microphone this micro Oh close microphone and a distant microphone doing these different tests on Uh we should do a measurement in here I g think we never have I think I Hmm guess uh point seven point eight seconds f uh something like that But you know this room So OK Uh But the other thing is putting in w I was using the word reverberation in two ways also putting in uh a taking out some reverberation but putting in something because he has averages over multiple windows stretching out to twelve seconds which are then being subtracted from the speech And since you know what you subtract sometimes be be subtracting from some larger number and sometimes you won't And So you can end up with some components in it that are affected by things that are seconds away Uh and if a low energy compo portion you might actually hear some funny things Yeah O o one thing um I noticed is that um the mean subtraction seems to make the PZM signals louder after been So I was wondering is it possible that one reason it helped with the Aurora baseline system is just as a kind of gain control Cuz some of the PZM signals sound pretty quiet if you don't amplify them I don't see why why your signal is louder after processing because yo Yeah I don't know why uh either Yeah I don't think just multiplying the signal by two would have any effect Oh OK Yeah I mean Well think well if you really have louder signals what you mean is that you have better ratio So if what doing is improving the ratio then it would be better But just it being bigger if It with i same i it ratio affect things Yeah OK No Well the system is use the absolute energy so a little bit dependent on on the signal level But not so much I guess Well yeah But trained Mmm and tested on the same thing So if the if the if you change in both training and test the absolute level by a factor of two it will n have Yeah effect Did you add this data to the training set for the Aurora Or you Uh just tested on this Um Did I w what Sorry Well Morgan was just saying that uh as long as you do it in both training and testing Yeah shouldn't have any effect But I I was I sort b that you just tested with this data You didn't train it also I Right I trained on clean I I did the mean subtraction on clean But I didn't I sure see clean ti any louder I only remember noticing it made the um PZM signal louder OK Well I don't understand then Huh Yeah I don't know If if like if trying to find a a reverberation filter it could be that this reverberation filter is making things quieter And then if you take it out that taking it out makes things louder I mean Uh no I mean uh nothing inherent about removing if really removing The mean uh r uh then I don't see OK how Yeah would see it Yeah louder OK So So I should it maybe might listen just to some stuff again Yeah It might just be some artifact of the processing that that uh if Uh yeah Oh don't OK Eh if there could be something like uh for s for the PZM data uh you know if occasionally uh somebody hits the table or something you could get a spike Uh just wondering if something about the um you know doing the mean normalization where uh it it could cause you to have better ratio Um Well you know there is this Wait a minute It it i maybe i If um Subtracting the the mean log spectrum is is is like dividing by the spectrum So depending what you divide by if your if s your estimate is off and sometimes getting a small number you could make it bigger So just a a question of It it could be that some normalization missing or something to make it Uh y think it shouldn't be larger but maybe in practice it is Hmm something to think about I don't know I had a question about the system the SRI system So you trained it on But except this exactly the same system as the one that was tested before and that was trained on Macrophone Right So on it gives you one point two percent error rate and on Macrophone still O point eight