Ryan wrote:
(Scott Dorsey) wrote in message ...

Hi Scott. How have you been? Heard anymore Sonic Youth of late?

I'm listening to Toots and the Maytals as I type this...

No. ANY arbitrary waveform can be decomposed down to sine waves. When you
put the sines back together, you can reconstitute the original wave. This is
the WHOLE POINT of the Fourier series. The time domain and frequency domain
representations of the waveform are equivalent and you can convert from one
to the other and back with impunity.

So what I have to do is perform FFT on each of my sound "samples", the
squeak of a vilon played behind the bridge, a viol's "dry string"
sounds, regular arco, pizzicato, etc, ect, all the ohter instruments,
etc. And then perform an FFT on any given sound file I'm interested
in emulating. After that, what kind of math would be used to sort
through all the samples and figure what goes best where?

I'm not sure this will really do what you want, but you can try it. You
could just do a standard correlation coefficient and see how close they
come.

Then again, you could probably just do a correlation coefficient on the
samples themselves. That might be fun to look at.

Samplitude features an FFT analyses window. It just looks like a
regular EQ anlysis to me. Is it the case that if I take each
frequency as a sine wave and apply it to the given amplitude that I
will have achieved X's sound? Is there anyway to simplify that? Even
the simplest natural sounds have about a 5khz range. Do I have to
create 5000 individual sine waves? The FFT graph only shows frequency
over time, How do I find out about the relationships between the
frequencies as far as timming? For example say a put a sine wave at
2Khz and 1Khz. Obviously the 2Khz occilates twice as fast as the 1
Khz, but beyond that, the starting/ending points (where y=0) might not
sink up. The 2Khz sine may start, say, 300ths of a second after the
1Khz. I don't think info like this can be found out by the FFT
window, can it?

No, you probably want a tool like matlab. How many terms you want to
calculate out to depends on how good an approximation you want. I think
that the number of terms that you're going to get is going to be larger
than the number of samples in the original file for most arbitrary sounds.
You can decide to reduce this by bandlimiting the original signal, though.
--scott
--
"C'est un Nagra. C'est suisse, et tres, tres precis."