Hi Magnus,
On 15.05.22 19:37, Magnus Danielson via time-nuts wrote:
This is a result of using real-only values in the complex Fourier
transform. It creates mirror images. Greenhall uses one method to
circumvent the issue.
Can't quite follow on that one. What do you mean by "mirror images"?
Do
Hi Matthias,
On 2022-05-14 12:30, Matthias Welwarsky wrote:
On Samstag, 14. Mai 2022 18:43:13 CEST Carsten Andrich wrote:
However, even for the 2^16 samples used by the CCRMA snippet, the filter
slope rolls off too quickly. I've attached its frequency response. It
exhibits a little wobbly 1/f
Hi Carsten,
On 2022-05-14 11:38, Carsten Andrich wrote:
Hi Magnus,
On 14.05.22 08:59, Magnus Danielson via time-nuts wrote:
Do note that the model of no correlation is not correct model of
reality. There is several effects which make "white noise" slightly
correlated, even if this for most
Hi Matthias,
On 2022-05-14 08:58, Matthias Welwarsky wrote:
On Dienstag, 3. Mai 2022 22:08:49 CEST Magnus Danielson via time-nuts wrote:
Dear Matthias,
Notice that 1/f is power-spectrum density, straight filter will give you
1/f^2 in power-spectrum, just as an integration slope.
One approach
On Samstag, 14. Mai 2022 18:43:13 CEST Carsten Andrich wrote:
> However, even for the 2^16 samples used by the CCRMA snippet, the filter
> slope rolls off too quickly. I've attached its frequency response. It
> exhibits a little wobbly 1/f power slope over 3 orders of magnitude, but
> it's
Hi Magnus,
On 14.05.22 08:59, Magnus Danielson via time-nuts wrote:
Do note that the model of no correlation is not correct model of
reality. There is several effects which make "white noise" slightly
correlated, even if this for most pratical uses is very small
correlation. Not that it
On 14.05.22 16:58, Matthias Welwarsky wrote:
I went "window shopping" on Google and found something that would probably fit
my needs here:
https://ccrma.stanford.edu/~jos/sasp/Example_Synthesis_1_F_Noise.html
Matlab code:
Nx = 2^16; % number of samples to synthesize
B = [0.049922035
On Dienstag, 3. Mai 2022 22:08:49 CEST Magnus Danielson via time-nuts wrote:
> Dear Matthias,
>
> Notice that 1/f is power-spectrum density, straight filter will give you
> 1/f^2 in power-spectrum, just as an integration slope.
>
> One approach to flicker filter is an IIR filter with the
Hi Carsten,
On 2022-05-13 09:25, Carsten Andrich wrote:
On 11.05.22 08:15, Carsten Andrich wrote:
Also, any reason to do this via forward and inverse FFT? AFAIK the
Fourier transform of white noise is white noise, [...]
I had the same question when I first saw this. Unfortunately I don't
On 11.05.22 08:15, Carsten Andrich wrote:
Also, any reason to do this via forward and inverse FFT? AFAIK the
Fourier transform of white noise is white noise, [...]
I had the same question when I first saw this. Unfortunately I don't have a good
answer, besides that forward + inverse ensures
On 10.05.22 10:37, Neville Michie wrote:
The use of forward then reverse Fourier transforms is one of the most important
achievements of the Fourier transform. When one data set is convolved with
another data set, it appears impossible to undo the tangle.
But if the data is transformed into the
On Tue, 10 May 2022 08:20:35 +0200
Carsten Andrich wrote:
> If you happen to find the paper, please share a reference. I'm curious
> about implementation details and side-effects, e.g., whether
> implementing the filter via circular convolution (straightforward
> multiplication in
The use of forward then reverse Fourier transforms is one of the most important
achievements of the Fourier transform. When one data set is convolved with
another data set, it appears impossible to undo the tangle.
But if the data is transformed into the Fourier domain, serial division can
First, thanks to everyone who chimed in on this highly interesting topic.
On 04.05.22 18:49, Attila Kinali wrote:
FFT based systems take a white, normal distributed noise source,
Fourier transform it, filter it in frequency domain and transform
it back. Runtime is dominated by the FFT and thus
Hi,
Could not agree more on this point.
It's even to the point we have two standards for it, the IEEE Std 1139
for the basic measures and noises, and then IEEE Std 1193 for the
"environmentals", or rather, the rest.
Both is being revisioned and 1139 just went out for re-balloting process
On Wed, 04 May 2022 17:07:03 -0700
Hal Murray wrote:
> What sort of times and memory are interesting?
A lot of times! :-P
I think last time I generated them I had to run them on a machine with 256GB
RAM.
So... probably 200G of data?
> You can rent a cloud server with a few hundred gigabytes
Hi
The most basic is the “phase pop” that is not modeled by any of the
normal noise formulas. The further you dig in, the more you find things
that the models really don’t cover.
Bob
> On May 4, 2022, at 11:50 AM, Attila Kinali wrote:
>
> Hoi Bob,
>
> On Tue, 3 May 2022 16:23:27 -0500
>
att...@kinali.ch said:
> FFT based systems take a white, normal distributed noise source, Fourier
> transform it, filter it in frequency domain and transform it back. Runtime is
> dominated by the FFT and thus O(n*log(n)). There was a nice paper by either
> Barnes or Greenhall (or both?) on
Magnus, Attila, Bob,
thanks again for the inspirational posts, truly appreciated.
However. I'm looking for something reasonably simple just for the purpose of
GPSDO simulation. Here, most of the finer details of noise are not very
relevant. I don't really care for PSD, for example. What I'm
Hoi Bob,
On Tue, 3 May 2022 16:23:27 -0500
Bob kb8tq wrote:
> The gotcha is that there are a number of very normal OCXO “behaviors” that
> are not
> covered by any of the standard statistical models.
Could you elaborate a bit on what these "normal behaviours" are?
On Tue, 3 May 2022 08:06:22 -0700
"Lux, Jim" wrote:
> There's some papers out there (mentioned on the list in the past) about
> synthesizing colored noise. Taking "White" noise and running it through
> a filter is one approach. Another is doing an inverse FFT, but that has
> the issue of
Dear Matthias,
On 2022-05-03 10:57, Matthias Welwarsky wrote:
Dear all,
thanks for your kind comments, corrections and suggestions. Please forgive if
I don't reply to all of your comments individually. Summary response follows:
Attila - yes, I realize temperature dependence is one key
Hi
The gotcha is that there are a number of very normal OCXO “behaviors” that are
not
covered by any of the standard statistical models. Coping with these issue is
at least
as important at working with the stuff that is coved by any of the standard
statistical
models ….
Bob
> On May 3,
On 5/3/22 1:57 AM, Matthias Welwarsky wrote:
Magnus, Jim - thanks a lot. Your post encouraged me to look especially into
flicker noise an how to generate it in the time domain. I now use randn() and
a low-pass filter. Also, I think I understood now how to create phase vs
frequency noise.
On 5/2/22 7:03 PM, Magnus Danielson via time-nuts wrote:
Hi Jim,
Thanks for the corrections. Was way to tired to get the uniform and
normal distributions right.
rand() is then by classical UNIX tradition is generated as a unsigned
integer divided by the suitable (32th) power of two, so the
Hi Jim,
Thanks for the corrections. Was way to tired to get the uniform and
normal distributions right.
rand() is then by classical UNIX tradition is generated as a unsigned
integer divided by the suitable (32th) power of two, so the maximum
value will not be there, and this is why a small
On 5/2/22 6:09 PM, Magnus Danielson via time-nuts wrote:
Matthias,
On 2022-05-02 17:12, Matthias Welwarsky wrote:
Dear all,
I'm trying to come up with a reasonably simple model for an OCXO that
I can
parametrize to experiment with a GPSDO simulator. For now I have the
following
matlab
Matthias,
On 2022-05-02 17:12, Matthias Welwarsky wrote:
Dear all,
I'm trying to come up with a reasonably simple model for an OCXO that I can
parametrize to experiment with a GPSDO simulator. For now I have the following
matlab function that "somewhat" does what I think is reasonable, but I
Hi
….. except that having done this for many decades on hundreds of
designs, , a single data set from a real OCXO is likely to show you
things that millions of simulations from a formula will somehow miss ….
Bob
> On May 2, 2022, at 5:13 PM, Greg Maxwell wrote:
>
> On Mon, May 2, 2022 at
On Mon, May 2, 2022 at 10:01 PM Bob kb8tq wrote:
> By far the best approach is to use actual data. Grab a pair of OCXO’s and
> compare them. A single mixer setup is one easy ( = cheap ) way to do it. You
> will get the sum of the two devices, but for simulation purposes, it will be
> *much*
>
Hi
By far the best approach is to use actual data. Grab a pair of OCXO’s and
compare them. A single mixer setup is one easy ( = cheap ) way to do it. You
will get the sum of the two devices, but for simulation purposes, it will be
*much*
closer to reality than anything you can brew up with a
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