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Matt Southall wrote
>This is something I've been interested in for a while. As you point out,
>using a perceptron for each individual sample is a bit of a pain. One
>thing you might like to look at is recurrent networks.
yes, that was my first thought too. But recursive nets can hold AT MOST the
past six values, which is fine if youre training a 3 binary digit sequence,
but its impracticable for audio at 44.1 kHz (some reduction of the input
data can be performed, but I dont believe you can reduce audio data to the
point where 6 past values would be enough...)
But if you consider applying this line of thought to _midi_ data, instead of
sample data, then its a whole different scenario...
>I've just had a paper accepted for 'Simulated Adaptive Behaviour 98' on
>the subject of 'Temporal Pattern Learning in a Spiking Neural Network',
is it online? (would like to see it)
>and I'd be interested to discuss the issues involved (privately, it's a
>little off topic) with anyone else who has attempted anything along these
>lines
sure, I'd like that
>I think the first step in improving your network
>would be to move to a freq/time domain.
actually thats what I'm doing now :) this example was primarly meant to
assert how far could I go in computational power using csound; no practical
use whatsoever
concerning csound, some leads I'm working on include combining delay units
to give the net some recursiveness (several points of the sample are
presented to the net at each instant, so the net aint recursive, but the
input is presented in a recurrent form); other hipotesis are using the
csound utilities to extract audio info that would be more suitable to be
presented to the net; maybe pvanal can be used for that, but I'm still
studying the implications.
Of course my next attempts will use back-prop and hidden layers, but I must
reduce the presented audio info so that I dont have to deal with such big
arrays
One thing I know, is that this will still hold many challanges for my future
csound programming
pedro |