we could put it in.
Mm - hmm.  Mm - hmm.
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.