I think it's interesting for instance for early echoes in a reverb.
For longer sequences it seems to become very self-similar?
I only looked into the thing under "Additive recurrence", the rest is
totally above my head, and played around with this a little bit, since
it's basically a random number generator with "bad parameters" there.
I tried similar things before with the golden ratio, bascially even the
same thing I just realize.
Like panning in a seemingly random fashion with golden ratio mod 1.
With little sucess in reverbs, though, for instance most of the time
it's not a great
idea to just tune delay lengths to golden ratios...
But maybe it's useful for setting delay lengths in a different way?
Just seeing the similarity between a classical reverb algorithm and
random number generators
with the feedback loop acting as mod operator.. didn't see it like that
before
Did anybody build a reverb based on a random generator algorithm?
Or are reverbs just that and it just never occured to me?
What I also wonder is the difference between a sequence like that (or
any random sequence)
and a sequence thats synthesized with FFT to be flat but with random phases.
I wonder what's better in terms of what and why, when it comes to reverb
and/or convolution,
Am 30.07.2016 um 19:57 schrieb Patric Schmitz:
Hi,
On 07/28/2016 08:43 PM, gm wrote:
My problem was that a short segment of random isn't spectrally
straigh-line flat.
On 07/30/2016 07:22 PM, gm wrote:
Just a short sequence of random numbers really exhibits large
formant like fluctuations .
I tried following this discussion even though, admittedly, most
of it is way over my head. Still, I wonder if the problem of
short random sample sets being too non-uniformly distributed
could be alleviated somehow, by not using white noise for the
samples, but what they call a low-discrepancy quasi- or subrandom
sequence of numbers.
https://en.wikipedia.org/wiki/Low-discrepancy_sequence
I heard about them in a different context, and it seems their
main property is that they converge against the equally
distributed limit distribution much quicker than true random
samples taken from that distribution. Maybe they could be useful
here to get a spectrally more flat distribution with a fewer
number of samples?
As said, I'm by far no expert in the field and most of what has
been said is above my level of understanding, so please feel free
to discard this as utter nonsense!
Best regards,
Patric Schmitz
_______________________________________________
dupswapdrop: music-dsp mailing list
music-dsp@music.columbia.edu
https://lists.columbia.edu/mailman/listinfo/music-dsp
_______________________________________________
dupswapdrop: music-dsp mailing list
music-dsp@music.columbia.edu
https://lists.columbia.edu/mailman/listinfo/music-dsp
