because  it seems like  the modulation um spectrum tells you a lot about the intelligibility of  of certain um words and stuff So , um. Yeah. Just that 's about it.
OK.
OK. And um so I 've been looking at Avendano 's work and um uh I 'll try to write up in my next stat status report a nice description of  what he 's doing , but it 's  it 's an approach to deal with  reverberation or that  the aspect of his work that I 'm interested in the idea is that um    normally an analysis frames are um  too short to encompass reverberation effects um in full. You miss most of the reverberation tail in a ten millisecond window and so   you  you 'd like it to be that  um  the reverberation responses um simply convolved um in , but it 's not really with these ten millisecond frames cuz you j But if you take , say , a two millisecond  um window  I 'm sorry a two second window then in a room like this , most of the reverberation response  is included in the window and the  then it um  then things are l more linear. It is  it is more like the reverberation response is simply c convolved and um   and you can use channel normalization techniques  like uh in his thesis he 's assuming that the reverberation response is fixed. He just does um  mean subtraction , which is like removing the DC component of the modulation spectrum and  that 's supposed to d um deal  uh deal pretty well with the um reverberation and um  the neat thing is you can't take these two second frames and feed them to a speech recognizer um  so he does this  um  method training trading the um  the spectral resolution for time resolution  and um  come ca uh synthesizes a new representation which is with say ten second frames but a lower s um  frequency resolution. So I don't really know the theory. I guess it 's  these are called " time frequency representations " and h he 's making the  the time sh um finer grained and the frequency resolution um less fine grained.
Mm - hmm.