Here's a histogram generator (binner) that shows the distribution of
[gaussianoise]. Using it I can quickly see that [gaussianoise2] is too
peaked around zero and that [gaussianoise3] chops the tails off when the
scale is low.
If you have uniformly distributed raindrops falling, any given area will
receive a number of raindrops that clusters about the mean in a normal
distribution, just as if you first bin the number of occurrences of each
value of white noise, then bin the resulting counts, the histogram of
the counts will look like a bell curve centered at the mean count.
Martin
Andy Farnell wrote:
GEM is broken here, but thanks for the info Marius.
I'm reading through the docs for R at the moment.
It makes lovely plots, but haven't figured how to get
my data in to it yet...
JFYI the application is rainfall. Many papers I read describe
rainfall as Gaussian.
I know from physical analysis that raindrops are uniform in size
and velocity for any local sample, so I've realised this distribution
is about how they fall within an area and pondering how a
distribution can be Gaussian in 2D.
Thing is, I can't figure out any good reason why rain should
by anything other than uniformly distributed ! :(
When I use Martins second patch with a thresholding function
to trigger droplet sounds, it does sound a lot more like
real rainfall than a uniformly triggered model.
I'm in one of those grey areas where I half understand what I'm
doing, which is a dangerous place to be.
Anybody know of cool papers I might have missed on the
distribution of rain drops and the effect on their sound?
Thanks,
Andy
On Sun, 16 Mar 2008 15:43:34 -0400
marius schebella <[EMAIL PROTECTED]> wrote:
from the first equation that andy posted, I produced a gem
representation. the box muller noise seems wrong, because it does not
use the whole range but is shifted to the negative side.
note, this is not a distribution of frequencies, but of noise values..
marius.
Martin Peach wrote:
Oh no that's wrong isn't it :(
The log is necessary to keep the distribution normal, and the range is
going to get wider the closer to zero the radius is allowed to get.
The attached patch has a scale adjustment...
Still I wonder what kind of distribution gaussianoise2 gives, it's not
just white.
Martin
Martin Peach wrote:
Charles Henry wrote:
On Sun, Mar 16, 2008 at 11:16 AM, Martin Peach
<[EMAIL PROTECTED]> wrote:
(gaussianoise has occasional values that exceed [-1 ... 1], which I
suppose is normal...white noise is always on [-1...1])
That's true. With the Box-Muller method, there is the log(~U1) term,
but you can always just add a small value to U1, which will truncate
your distribution. The size of the small value can be calculated to
fit with any given threshold.
I think it's really because the Box-Muller method selects random
numbers in pairs which map to points in a unit square on the plane,
but then selects only those points which are inside the unit circle,
something that the pd patch doesn't do (how to resample points in a
dsp vector until they are in range?). The attached patch shows the
straightforward way of doing it by simply selecting a random radius
and angle and returning the resulting y coordinate as the random
number. The results are always on [-1,1].
I don't think sin~ will be any slower than log~.
Martin
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