Re: Would this approach be possible (for analog modeling)?
- From: "Jon Slaughter" <Jon_Slaughter@xxxxxxxxxxx>
- Date: Mon, 30 Jul 2007 07:10:16 -0500
"Rock Lobster" <email@xxxxxxxxxxxxxxxxxxxxx> wrote in message
news:etKdnem5FZVgHTDbnZ2dnUVZ_hOdnZ2d@xxxxxxxxxxxxxxx
Hello,
so far I didn't read anything technical about analog modeling, but I
thought about some very simple and straight-forward approach and I wonder
if that would work (at least to a semi-satisfying level) or if this would
result in a sonic catastrophy :)
How about creating a white-noise sound, which gradually gains volume from
0 to 10 (or maybe to 11? :), and then running this sound through a guitar
amplifier. The amp would be set to a good sounding distorted tone, but the
equalizer should maybe left flat.
Then the whole thing would be recorded and analyzed with FFT. The spectrum
for each volume would be saved in RAM.
Then, a random sound (preferably a guitar signal) would be played and also
FFTed, and the stuff we saved in RAM would be applied to the signal,
meaning that every frequency's volume would be changed according to the
"recorded" sound.
Would that work? The distortion level could then be altered just by
changing the input volume, and an equalizer could be built on top.
There are methods that use white noise to model nonlinear devices but I
don't know much about it. I remember finding a pdf about how to do it a long
time ago but don't remember to much about it. Essentially because the device
doesn't follow the superposition law you cannot decompose a signal into
simpiler ones and then put them back together.
For example, say you input f(t) = cos(wt) + cos(qt) then its not the same as
first inputing cos(wt) and then cos(qt) and then adding the results of the
device to get f(t). The reason is because the amplifier is not linear.
Now, there was some heavy math in that paper that dealt with what they
called volterra kernels and how to use them to model such things
approximately using white noise. I'm sure if you google you'll be able to
find some more info.
.
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