Yeah. Yeah. So , that 's good. So , why don't we just , uh , um  I think starting   starting a w couple weeks from now , especially if you 're not gonna be around for a while , we 'll  we 'll be shifting more over to some other   other territory. But , uh , uh ,  uh , n not  not so much in this meeting about Aurora , but  but , uh , uh , maybe just , uh , quickly today about  maybe you could just say a little bit about what you 've been talking about with Michael. And  and then Barry can say something about  what   what we 're talking about.
OK. So Michael Kleinschmidt , who 's a PHD student from Germany ,  showed up this week. He 'll be here for about six months. And he 's done some work using  an auditory model  of , um ,  human hearing , and  using that f uh , to generate speech recognition features. And  he did  work back in Germany  with , um , a toy recognition system  using , um , isolated  digit recognition  as the task. It was actually just a single - layer neural network  that classified words  classified digits ,  in fact. Um , and  he tried that on  I think on some Aurora data and got results that he thought  seemed respectable. And he w he 's coming here to u u use it on a  uh , a real speech recognition system. So I 'll be working with him on that. And , um , maybe I should say a little more about these features , although I don't understand them that well. The  I think it 's a two - stage idea. And , um ,  the first stage of these features correspond to what 's called the peripheral  auditory system. And  I guess that is like  a filter bank with a compressive nonlinearity. And  I 'm - I 'm not sure what we have @ @ in there that isn't already modeled in something like ,  um ,  PLP. I should learn more about that. And then  the second stage  is , um ,  the most different thing , I think , from what we usually do. It 's , um    it computes features which are ,  um ,  based on  sort of like based on diffe different w um , wavelet basis functions  used to analyze  the input.  So th he uses analysis functions called  Gabor functions , um ,  which have a certain  extent , um ,  in time and in frequency. And  the idea is these are used to sample ,  um , the signal in a represented as a time - frequency representation. So you 're  sampling some piece of this time - frequency plane. And , um ,  that ,  um , is  is interesting , cuz ,  @ @ for  for one thing , you could use it ,  um , in a  a multi - scale way. You could have these  instead of having everything  like we use a twenty - five millisecond or so analysis window ,  typically , um , and that 's our time scale for features , but you could   using this , um , basis function idea , you could have some basis functions which have a lot longer time scale and , um , some which have a lot shorter , and  so it would be like  a set of multi - scale features. So he 's interested in , um  Th - this is  because it 's , um  there are these different parameters for the shape of these  basis functions ,  um   there are a lot of different possible basis functions. And so he   he actually does  an optimization procedure to choose an   an optimal set of basis functions out of all the possible ones.
Hmm. H What does he do to choose those ?
The method he uses is kind of funny  is ,   um ,  he starts with  he has a set of M of them. Um , he  and then  he uses that to classify  I mean , he t he tries , um ,  using  just M minus one of them. So there are M possible subsets of this  length - M vector. He tries classifying , using each of the M  possible sub - vectors.