On Tue, Nov 02, 2010 at 06:13:49PM -0400, brandon zeeb wrote:
Great! Unfortunately, I'm not entirely sure how to realize this in Pd. Can
you help me out with a little example?
Like in the attachement maybe. The [pd roundit] doesn't do a floor, but a
real rounding. Rounding or flooring is at
could you give examples of idealized input and output for cases 1-4?
im not sure I understand what exactly you want...
interested greetings!
Ludwig
On 1 November 2010 13:09, brandon zeeb zeeb.bran...@gmail.com wrote:
Hey All,
I've been burning my brain over this issue lately and I can't seem
I've attached my best attempt at recreating this effect, the attached PNG
will be used as a reference.
Given the distance d1 and d2, these distances are usually identical in a
traditional bitcrush or simple quantization. I would like to be able to
vary the distance between points of an incoming
This is even better. If I could minimize the jumps around Y = 0.5 to -0.5
It'll be exactly what I'm looking for... or a start at least.
Do you see what I mean now? See how the amount of quantization changes with
Y and a minimum quantization value?
I think I'm getting towards the answer now...
So you want amplitude 'a' dependant quantization size 'q' ? take your
chosen q(a); in your example it seems you want a simple line:
q=q(0)-k*a;
define f(a) as integral of 1/q from a=0 to a; also calculate the
inverse of f(a) i.e. a(f);
now for each sample do: out=a(round(f(in))) where round is
The reason you use the inverse is so that the amplitude remains the
same albeit quantized. The reason we use another function before
flooring is to distritube the floor levels.But afterwards we need to
bring the values back to their original place
On 2 November 2010 19:37, Ludwig Maes
And we want f' to be 1 (integer step) / (per) quantization size (for
that amplitude)
On 2 November 2010 19:41, Ludwig Maes ludwig.m...@gmail.com wrote:
The reason you use the inverse is so that the amplitude remains the
same albeit quantized. The reason we use another function before
flooring
Watch out in my numeric example, I was a bit careless and the q I
chose continues to increase for more and more negative amplitudes!
On 2 November 2010 19:44, Ludwig Maes ludwig.m...@gmail.com wrote:
And we want f' to be 1 (integer step) / (per) quantization size (for
that amplitude)
On 2
Great! Unfortunately, I'm not entirely sure how to realize this in Pd. Can
you help me out with a little example?
Thanks
On Tue, Nov 2, 2010 at 2:37 PM, Ludwig Maes ludwig.m...@gmail.com wrote:
So you want amplitude 'a' dependant quantization size 'q' ? take your
chosen q(a); in your
Hey All,
I've been burning my brain over this issue lately and I can't seem to come
up with an elegant solution, and stay with me here as I attempt to explain
it best I can. For me and my needs, being able to quantize an arbitrary
signal to any arbitrary series is the Holy Grail (and I'm not
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