[HN Gopher] Why can't you design noise in frequency space?
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Why can't you design noise in frequency space?
Author : ingve
Score : 136 points
Date : 2021-12-30 12:35 UTC (10 hours ago)
(HTM) web link (blog.demofox.org)
(TXT) w3m dump (blog.demofox.org)
| LargoLasskhyfv wrote:
| I wonder if he ever heard of the (*)
| https://en.wikipedia.org/wiki/ANS_synthesizer and its modern
| implementation (:) https://warmplace.ru/soft/ans/ ?
| cozzyd wrote:
| No time to play with this at the moment but I suspect the problem
| is that the author didn't try generating the real and imaginary
| parts. You can't treat the magnitude the same way you treat
| amplitudes.
|
| Also I don't see if the DC and Nyquist terms are forced to be
| real (otherwise the IFT won't be real)?
| madengr wrote:
| mistercow wrote:
| > First we make N complex values from polar coordinates that
| have a random angle 0 to 2pi and a random radius from 0 to 1.
|
| This is what jumped out to me as suspicious. This looks just
| like a naive (and wrong) algorithm for generating random points
| in a circle. The simplest correct way to do this in the circle
| case is rejection sampling. In this context, that would mean:
| generate a random real and imaginary part to get z, and retry
| until |z| <= 1.
| midjji wrote:
| Then simplest way to do so is to sample a normal distribution
| twice and normalize the length. Its significantly faster too.
| im3w1l wrote:
| That gives r=1. They want r<=1.
| midjji wrote:
| Ah missed that
| ssfrr wrote:
| The goal is not well-defined here. If the goal is to generate
| uniformly-distributed points in a circle then this algorithm
| is wrong, but it's not clear that's actually what they want.
|
| Generating a spectrum with a given magnitude distribution and
| uniform phase distribution is pretty common (at least in
| audio).
| cozzyd wrote:
| Yes, though e.g. if you want white nose you'd use a
| Rayleigh distribution for magnitude rather than a Gaussian
| as you would use for real and imaginary parts.
| dahart wrote:
| > I suspect the problem is that the author didn't try
| generating the real and imaginary parts.
|
| I'm not sure I understand what you mean. He definitely does
| describe generating a complex number, and ensuring the
| imaginary parts are designed to produce an output image with
| real-only values after the FT. Can you elaborate on how
| magnitudes and amplitudes are being conflated?
|
| The problem, as I understand it, is the output is Gaussian
| distributed rather than uniform, not a simple bug or misuse of
| the DFT like you assume. Perhaps this implies that a using
| white noise source in the frequency domain is the issue, maybe
| the forward transform of blue noise is not white along the
| outside of frequency space, and so generating a frequency space
| image using white noise might not be expected to work?
|
| Maybe worth playing with it when you have time?
| cozzyd wrote:
| All I mean is that the statistics of the magnitude are
| different than the author expects, probably. You can
| certainly generate complex numbers with the magnitude, but
| picking from a uniform distribution is unlikely to generate
| what you want.
|
| Also, it will certainly be a problem if the DC/Nyquist terms
| aren't pure real, though maybe that is actually being done
| and I missed it.
| dahart wrote:
| > picking from a uniform distribution is unlikely to
| generate what you want.
|
| I see now, and I think this is a good point. Maybe I just
| said almost the same thing. Without looking at frequency
| plots, if I just think about what I expect to get with a
| DFT of high-pass filtered white noise, I'd assume it's
| blue-ish in the sense of having less low frequency, but I
| don't think I would expect it to produce the same perfectly
| even spread that the void-and-cluster algorithm produces.
| It seems like this implies that a forward DFT of a void-
| and-cluster blue noise texture has some structure that
| might be hard to see in frequency space, we can't assume
| frequency noise that looks white really is white, and maybe
| there is a relationship between the frequency and phase
| components that just aren't met by picking a random angle &
| radius.
| cozzyd wrote:
| Yeah, looking up void and cluster, I think it's clear the
| phases won't be uncorrelated between frequencies, though
| may not be easy to describe in the frequency domain.
| [deleted]
| ajot wrote:
| What are the use cases of generating different kind of noises
| (e.g. white, blue and red)? I've only heard about them as
| background noises to fall asleep or focus while working/studying.
|
| Is there something ininformation or signal theory that benefits
| from creating truly random (or random but with some structure to
| it like blue noise not having lower frequencies) noise? As a
| chemist, I always model real-world noise with a gaussian
| distribution, so I don't really get where this could br used.
| mzs wrote:
| blue noise dither:
| http://www.imaging.org/site/PDFS/Papers/1999/RP-0-93/1786.pd...
| the__alchemist wrote:
| I'm using pink noise in an audiogram, to determine if users can
| hear sounds with it as background.
| ocharles wrote:
| For one real application of blue noise in particular, see
| https://youtu.be/Ge3aKEmZcqY?t=1350 (22:30 is a good starting
| point). Here Casy explains a grass planting algorithm for
| games, where white noise wouldn't be suitable.
| iamwil wrote:
| Haha, I guess you and I just saw the same video.
| iamwil wrote:
| Blue noise is useful in games for generating more natural
| looking scenes. Here's an example of how to algorithmically
| place grass in the game "The Witness".
|
| https://youtu.be/Ge3aKEmZcqY?t=965
|
| So if you use white noise, it looks unnaturally clustered with
| patches. This is the part that discusses the noise.
|
| https://youtu.be/Ge3aKEmZcqY?t=1385
| anon_123g987 wrote:
| Also chemist here. Think about it the other way around, not
| synthesis but analysis. For example: _" The term "Brown noise"
| does not come from the color, but after Robert Brown, who
| documented the erratic motion for multiple types of inanimate
| particles in water."_
| (https://en.wikipedia.org/wiki/Brownian_noise)
| marktangotango wrote:
| Side note; what does a self described chemist do these days?
| Seems like Breaking Bad spurred a lot of interest in the
| field, but as someone who minored in chemistry many years
| ago, the actual job prospects seemed limited. And research
| seemed very stodgy (to me at least). If you don't mind
| sharing, what's your background and what types of things are
| you working on?
| kwijybo wrote:
| I'm a chemist. I've done a lot of food, beverage and water
| testing. Now I test dirt
| analog31 wrote:
| I'm not a chemist. (Worse, I'm a physicist). But I have
| several relatives who are chemists. And I work for
| chemists. I think there's an issue right now, which is that
| market demand for computer programmers has created a
| distorted view of all other occupations. That's not going
| away any time soon, and if it does, there will be some
| other "hot" occupation.
|
| I think what makes people want to become natural scientists
| is a genuine interest in how things work, plus either an
| innate or learned ability to "think" in a certain way that
| works for their field of study. I don't have a good way to
| describe it, but a sense that a chemist _thinks like a
| chemist._ A scientist is obsessed with learning how things
| work. The different fields are different approaches to
| finding that out, that work for their respective domains.
| Trying to figure out how a frog works by thinking like a
| physicist will result in a lot of dead frogs.
|
| The other post mentions food science. Stodgy, yes.
| Fascinating, you bet. Food isn't going away. The problems
| of making food abundant, pure, healthy, safe, and
| ecological, are going to get more and more challenging. It
| can be stodgy because we have to control our impulse to try
| dangerous experiments on human subjects, or make a mistake
| that brings down a production line or triggers a recall.
| But oddly enough there are people who get their excitement
| out of working within that constrained environment.
|
| You have to embrace the stodge. Something I've noticed
| about chemists, is that they tend to have the best
| discipline about running controlled, repeatable
| experiments. They keep the best notebooks.
|
| Chemistry is closely related to materials science. Any
| realistic development of a material beyond the basic
| research phase will require the involvement of a chemist.
| Likewise drug manufacturing, etc.
| anon_123g987 wrote:
| I don't think Chemistry in and of itself is a thing, or was
| it ever. It always has to be applied to something, and then
| the possibilities are endless. After all, everything is
| made of chemicals, isn't it? Personally, I'm in food, I
| also have an MSc in Food Engineering, and currently
| preparing to start a PhD in that area (NIR spectroscopy,
| Hyperspectral Image Analysis). But there is also the oil,
| pharma, bio, environmental, etc. areas. (Also, in British
| English "Chemist" means "Pharmacist". I always wondered,
| what they called a Chemist?)
| KineticLensman wrote:
| > Also, in British English "Chemist" means "Pharmacist".
| I always wondered, what they called a Chemist?
|
| (Brit here). A shop that sells pharma products is often
| colloquially called 'a chemists' but the people who work
| there are typically referred to as pharmacists.
| 'Chemistry' as studied at school and Uni is totally about
| chemicals in general and not drugs (which would be
| pharmacology). Someone who described themselves as a
| chemistry student or professor would almost inevitably be
| perceived as someone who is working with chemicals.
| anon_123g987 wrote:
| OK, thank you for clearing this up!
| whatshisface wrote:
| Chemists do the same things they always have, just for much
| less money than the same people would make today in
| software engineering. Here are some types of applied
| chemistry:
|
| - Deciding what ratio to mix things in for the countless
| liquid products that are combinations of already-discovered
| chemicals. Everything at the grocery store that comes in a
| jug or a bottle (cleaning products, hair conditioner, drain
| cleaner) falls in to this category.
|
| - Doing industrial research to improve existing processes.
| This would include discovering new catalysts, and trying
| out the endless permutations of solvents and conditions in
| which existing reactions take place, to optimize them for
| whatever the biggest operating costs are.
|
| - Figuring out how to recycle industrial chemicals and get
| the valuable stuff back out of sludge and effluent. This is
| a surprisingly big field with important consequences.
|
| - Working on specialty materials, like plastics and
| synthetic rubbers, that are not completely dissimilar from
| existing products but require a chemist to design them for
| specific, demanding applications.
|
| The fact that everything involving the physical world gets
| you paid way less for work that's way more difficult than
| programming will come back to bite us somehow, but it's
| hard to say when.
| littlestymaar wrote:
| Blue noise is useful for procedural generation, because it's
| what the human eye will naturally consider the "most randon"
| and the most pleasant. IIRC red noise appears when you have
| random walks or Brownian motion.
|
| > As a chemist, I always model real-world noise with a gaussian
| distribution
|
| Gaussian distribution is what you get when you have a lot of
| independent random events adding up (it's the central limit
| theorem), so it applies a lot in real life but it can also
| cause modeling issues if it's not how randomness appears in
| your system. (Please note that I have absolutely no knowledge
| on chemistry so I have no idea if it's something that could be
| relevant in your field)
| [deleted]
| dnautics wrote:
| Chemist here too but my ex was an sound consultant for an
| architectural firm. Iirc, different kinds of noises essentially
| are defined by the average amplitude (energy) vs frequency
| function of the rng. For architects, it can be important to
| model noise to ensure your structure complies with for example
| OSHA regulations. There can be code regulations too if you're
| in an urban place and say putting residential on top of street
| level commercial spaces, especially bars that might be open
| late. Finally, for gathering/events spaces you want to have a
| situation where a PA/presentation system doesn't have to work
| hard to go over conversation noise.
|
| The energy profiles for all of these scenarios is empirically
| determined and "well known" in the community.
|
| The ex helped model the "death star" auditorium space at the
| academy of motion picture arts and sciences in LA. IIRC it
| sounds really aquarium-ey during normal times and during events
| they roll out strategically placed dampeners.
| PragmaticPulp wrote:
| The difference is the spectral power distribution of the noise.
|
| White noise has a flat spectrum from low to high frequencies.
| It has the standard noise sound that most of us recognize from
| digital systems.
|
| Pink noise is shaped with a decreasing power as frequency
| increases. This results in more low frequency noise and less
| high frequency noise. This noise pattern occurs frequently in
| natural systems.
|
| Different colors of noise have different sounds to our ears, of
| course. The color naming scheme is loosely intended to map to
| light spectral distributions. For example, blue noise has a
| rising power with increasing frequency, similar to how blue
| light has more energy at higher frequencies (shorter
| wavelengths).
| HelloNurse wrote:
| Different spectrums can be tailored for signal processing
| schemes that want to add more noise at frequencies that need
| it and remove the noise with a filter when it has served its
| purpose. For example dithering adds enough extremely high
| frequency noise to obliterate unwanted patterns, then applies
| a lowpass filter that keeps all the "good" signal and removes
| the noise.
| jarenmf wrote:
| Sometimes certain measuring instruments can be assumed to have
| such profiles for noise, in this case generating noise is
| useful for Monte Carlo based modeling or uncertainty
| propagation.
| dahart wrote:
| One reason to use Blue noise in graphics / games / images is
| anytime you need a random number per pixel for, say, some Monte
| Carlo process, that will have a visible effect on the image and
| result in visible noise in the output. Using blue noise, the
| output will be visibly less "noisy" looking than when using
| white noise, due to blue noise having no low frequencies.
|
| Blue noise is best for situations where you only have the
| budget for 1 or 2 samples per pixel. The very low sample counts
| is where it performs considerably better than white noise. If
| you integrate with many samples, tens or hundreds or more, then
| the advantage of blue noise over other "colors" diminishes.
|
| The author of this post has some examples in other articles on
| his blog, for example using blue noise vs white noise for ray
| traced soft shadows. https://blog.demofox.org/2020/05/16/using-
| blue-noise-for-ray...
| nyanpasu64 wrote:
| So frequency-domain blue noise has a higher crest factor than
| void-and-cluster?
|
| Also I'm interested to see how closely the mean intensity (at
| each radius) of void-and-cluster blue noise actually matches a
| sinc or sinc-squared subtracted from a constant.
| enriquto wrote:
| No need for gradient descent or stuff. For these simple
| disributions you can design a target histogram and a target
| spectral decay and sample it.
| ssfrr wrote:
| Can you clarify? It's not clear to me how to target a
| particular distribution in time and frequency domain
| simultaneously.
| enriquto wrote:
| Think about the direct problem: you start with white noise
| with a distribution g such that you can compute its power
| spectrum (e.g. if g is Gaussian) , and then convolve it with
| a positive kernel k. You can write down explicitly the
| histogram and the power spectrum of the result, in terms of g
| and k. Now work backwards from it. I can find a reference if
| you need it.
| wg-throw-away wrote:
| For what it's worth, in digital communications, OFDM you could
| say "is designed in frequency space" and then passed through a
| FFT/IFFT and the output time-domain signal is what is sent over
| the air.
| anon_123g987 wrote:
| Although the author couldn't do it, the problem is obviously
| solvable, and not even that difficult.
|
| Theory: https://rjav.sra.ro/index.php/rjav/article/view/40/29
| [PDF]
|
| Matlab code (under the Functions tab):
| https://www.mathworks.com/matlabcentral/fileexchange/42919-p...
|
| This is for the 1D case, but since the Fourier transform is
| separable, it works identically in 2 (and any N) dimensions by
| performing the transform sequentially in every dimensions.
| lapinot wrote:
| You're pointing to something saying that power-law distributed
| noise can be generated by filtering white noise (and doing the
| filtering in the frequency domain, but that's really an
| implementation detail). OP actually wants to only do the
| inverse transform, generating the noise in the frequency domain
| already.
| anon_123g987 wrote:
| The Fourier transform of white noise is white noise, so that
| doesn't matter.
| lapinot wrote:
| Ah hm seems legit, didn't think it through. Weird why OP
| didn't manage then..
| midjji wrote:
| Because he is confusing the sample frequencies with the
| frequencies of the autocorelation.
| ssfrr wrote:
| Does that approach end up with a uniform distribution if values
| in the time domain? That seemed like the difficult part here.
| RicoElectrico wrote:
| I thought this was straightforward like in the audio domain? [1]
|
| Now, not all noises of the same amplitude spectrum are created
| equal. For instance LFSR sequences are only +-1 yet they have a
| white spectrum. The difference lies in phase.
|
| I wonder if generating random phases, then putting it through an
| optimizer to find lowest crest factor could work.
|
| Oh, and can someone chime in whether void-and-cluster masks
| guarantee that all values are covered, just like in Bayer mask?
|
| [1] https://zynaddsubfx.sourceforge.io/doc/PADsynth/PADsynth.htm
| [deleted]
| im3w1l wrote:
| Fourier theory is built on the assumption of everything being
| linear, and I suspect it can be hard to fit the constraint of 0
| <= v <= 1 into that framework. In audio they solve this by having
| headroom (not applicable here) or using dynamic range compression
| (might work), but that will change the frequencies a little bit.
| Histogram equalization could be something to try. It forces the
| values to be uniformly 0 to 1.
|
| This could also solve the issue of
|
| > the problem with the IDFT method is though... you get gaussian
| distributed values, not uniform, and the noise seems to be lower
| quality as well. If these issues could be solved, or if this
| noise has value as is, I think that'd be a real interesting and
| useful result
| anotheryou wrote:
| Trevor Wishart works in the spectral space quite a bit when
| editing.
|
| One of his sound art pieces (I'm sure many cuts and similarity
| matching were done automatic and spectrum based):
| https://youtu.be/DWkxPP6Ndng?t=473
|
| Showcasing the sounds of his software:
| https://youtu.be/f9swWsGgLB4
|
| The "Composers Desktop Project" also exists standalone with a
| paid front-end I think. Not sure anyone but him can use it well
| though :). It also has a loooong history (starting 1986)
| https://www.composersdesktop.com/history.html
|
| edit: his UI, sound loom, in "use" (at least you see how
| transforming to spectral space and re-synthesis are a thing, not
| much else though :).) : https://youtu.be/LypM6-WDjL8?t=620
| nobodywillobsrv wrote:
| Didn't read in detail and had a hard time understanding what
| their goal was but there are important things to understand
| regarding Fourier features (eigenfunctions, sin, cos, etc) and
| the Gaussian Kernel
|
| Start with something like this
| https://arxiv.org/pdf/1611.06740.pdf and reading links might
| help.
| stochtastic wrote:
| There is an interesting relationship between frequency domain
| filtering and the distributional properties of a signal, which I
| believe the author encounters: So interestingly,
| the IDFT method makes noise that is gaussian distributed. This
| kind of makes sense because we are filling out frequencies as
| uniform random white noise, which are turning into uniform random
| white noise sinusoids that are being summed together, which will
| tend towards a gaussian distribution as you sum up more of them.
| In contrast, the void and cluster method makes uniform
| distributed values which are perfectly uniform.
|
| One of the papers I'm most proud of co-authoring explains some
| aspects of this phenomenon [0] through the use of higher order
| spectra (the bispectrum, trispectrum, etc...) and how the
| geometry of frequency-domain filters affects skewness and excess
| kurtosis.
|
| [0] https://s3-us-
| west-2.amazonaws.com/arpdfs/Publications/Prois...
| a9h74j wrote:
| FWIW, in looking up higher-order spectra, I see that work at
| LIGO has resulted in an interesting toolkit and documentation:
|
| https://labcit.ligo.caltech.edu/~rana/mat/HOSA/HOSA.PDF
| Lichtso wrote:
| Two things which seem to be overlooked here:
|
| - When working with frequencies alone and ignoring the phases you
| kill half of the entropy of the signal.
|
| - Further more, when only the positive half of the frequencies is
| used and the negative half is mirrored with the complex
| conjugate, you kill half of the remaining entropy of the signal.
| anon_123g987 wrote:
| Your first point is spot on, but the second is not: for the
| signal to be real valued in the time domain, the negative and
| positive frequencies _have_ to be redundant in the complex
| valued frequency domain.
|
| Edited to add a link to a simple Matlab solution of this
| problem:
| https://www.mathworks.com/matlabcentral/fileexchange/42919-p...
| (click on the Functions tab to see the source code)
| Lichtso wrote:
| You are right, let me rephrase it: You would loose half of
| the remaining potential to encode entropy.
|
| Of course, if you start with a real valued signal, then you
| already lost that before even getting to the transform part.
| Or in other words you have to transform a signal twice as
| long as it needs to be, because half of it is mirrored and
| then discarded (I assume).
| ssfrr wrote:
| The goal is to generate a random spectrum that corresponds
| to a real-valued signal. If the positive and negative
| frequencies aren't conjugate-symmetric then the resulting
| signal after the IDFT will be complex-valued.
|
| You can think of it in terms of degrees of freedom. A real-
| valued length-N signal has N degrees of freedom. A length-N
| spectrum is complex-valued, meaning 2 degrees of freedom
| per frequency bin. When you constrain it to be conjugate-
| symmetric you bring the degrees of freedom back to N, which
| matches the real-valued signal.
| omegalulw wrote:
| The goal is to produce real valued noise - you won't get
| that if the spectrum is not mirrored.
| omegalulw wrote:
| I'm confused about the first point. Is it simply restating
| that Fourier coefficients are complex numbers?
| anon_123g987 wrote:
| Basically yes, but his statement is mixing up things a bit.
| The Discrete Fourier Transform transforms a complex vector
| of length N to another complex vector of length N. In the
| latter, each of the N elements corresponds to a frequency
| "bin" and, like complex numbers in general, can be
| represented either by a real and an imaginary part (like
| Cartesian coordinates), or by a phase (==atan(Im/Re)) and a
| magnitude (==sqrt(Re^2+Im^2)) (like polar coordinates).
| Obviously, whichever you choose, you need both components
| for unambiguous representation.
| sdenton4 wrote:
| To add a DIFFERENT point on handling phase: Overlap-add STFT
| actually has phase dependencies between adjacent patches. So
| generating independent random phase in frequency space tends to
| produce incompatible phase in adjacent patches. In audio the
| audio domain, this leads to audible distortion.
|
| What's (often) used in the audio domain is Griffin-Lim, in
| which you apply the ISTFT and STFT repeatedly to 'smooth out'
| the phase inconsistencies. It typically takes a long time to
| converge and is still not quite right. The main alternatives
| for audio are the new neural vocoders. But they are expensive
| to train, and it's not terribly clear to me that it's any
| better than using an existing blue noise algorithm for this
| specific problem.
| dahart wrote:
| > When working with frequencies alone and ignoring the phases
| you kill half the entropy of the signal.
|
| That's true, but why do you think this is happening in this
| post?
| ssfrr wrote:
| The author is sampling random magnitude and also random phase
| for each frequency bin, so I don't think anything is missed
| here.
| ssfrr wrote:
| I wonder if there's some iterative algorithm that would work
| here.
|
| When synthesizing audio from the short-time Fourier transform
| (STFT) sometimes you have unknown or noisy phase, and there's an
| algorithm called Griffey-Lim that's pretty common for finding the
| corresponding time-domain signal. You start in the STFT-domain
| and continually swap between time and STFT domains, each time
| fixing the STFT magnitudes. Eventually the phase tends to
| converge (but not sure if that's guaranteed).
|
| Maybe there's something similar here where you keep swapping back
| and forth while applying the blue spectrum and uniform sample
| histogram constraints (or partially applying them in a gradient-
| descent fashion).
|
| (Side note that there are other/better phase estimation algos for
| this problem, but Griffin-Lim is simple and relatively common)
| onos wrote:
| Newbie here trying to follow.
|
| The prior post I link below suggests that what's going on here is
| we are trying to find good points to sample an image. A challenge
| here is that the algorithms to generate the blue noise
| distributed sample points are slow. This motivates using an FT
| signal constructed by hand and then inverting this somehow to get
| sample points more quickly. But... he's finding that the result
| doesn't place sample points uniformly throughout an image. Is
| that right?
|
| How does one construct discrete sample points from the inverted
| FT?
|
| https://blog.demofox.org/2017/10/20/generating-blue-noise-sa...
| dahart wrote:
| Yep, you've got the right idea - the implied goal of trying to
| use the FT is to make blue noise texture generation faster, but
| it doesn't seem to work using the DFT. (Or maybe there are
| unmet constraints on how you need to generate the frequency
| space texture.)
|
| > How does one construct discrete sample points from the
| inverted FT?
|
| This is a good question! So one example the author has (in a
| different post) is how to use blue noise textures for ray
| traced shadows. The idea is at every pixel of your output
| image, trace a ray into the scene, then when it hits something,
| trace a ray toward the light to see if the pixel is lit or
| shadowed. Normally, you'd use a random number generator to pick
| a random sample on the hemisphere of the surface the first ray
| hits, and shoot a new ray in this random direction. You can
| instead grab your two random values from the red and green
| channels of a blue noise texture. The blue noise texture could
| have the same size as your desired output image, and you would
| use the same pixel id in the blue noise texture as the pixel id
| of your output image.
|
| That example is pretty easy, but I found out it can be
| surprisingly tricky to use blue noise textures in other ways,
| there are a limited number of ways you can use a blue noise
| texture effectively, and it's not always an easy drop-in
| replacement for a white noise random number generator. It's
| much harder to use blue noise for multiple samples per pixel,
| for example. It's harder to create a sequence of blue-
| distributed random numbers for use in a single integral.
| Another way to say that is that it would be difficult to
| generate your camera rays using blue noise.
| onos wrote:
| Thanks very much for that detailed explanation -- it helps a
| lot!
| henrikf wrote:
| Author seems to be looking to generate a blue noise texture for
| image sampling. I'm not familiar with them but it seems to be
| blue noise which also has uniformly distributed values in time
| domain. Generating white noise in frequency domain and
| multiplying with a frequency shape mask can generate noise with
| any frequency distribution, but it does not fill the uniform
| distributed values in time-domain requirement.
|
| If there are no other requirements than the frequency spectrum
| then generating the noise in frequency domain works fine.
| amelius wrote:
| If you view the IFFT as a random number generator, how do you
| change the seed of it?
|
| I.e., white noise has a flat spectrum. So if you take a flat
| spectrum back to noise (using IFFT), what determines the seed
| of this process?
| ssfrr wrote:
| Yeah I think that uniform time(space)--domain distribution
| constraint is the one that the author identifies as the main
| problem here.
| cjfd wrote:
| I studied physics when I was young and my first reaction was
| 'sure you can!' and 'I have done that kind of stuff some 20 years
| ago or so'. What looks to be the problem is that he wants a
| rather non-physical kind of noise. The noise is supposed to be
| between 0 and 1 distributed uniformly. Now, that really is not
| the kind of noise one expects or wants in physics in most cases.
| The values that are distributed in a gaussian way would seem to
| be much more sensible. O well, I suppose the thing he calls 'blue
| noise texture' really cannot be generated very well in frequency
| space....
| whatshisface wrote:
| If you do the phases wrong you will get pulses instead of
| noise. Obviously if you do the phases right you can do
| anything, because the Fourier transform is one to one.
| [deleted]
| a9h74j wrote:
| Even physical noise? I don't imagine you could create a random
| walk in frequency space, say if the steps taken had to be
| integer-valued.
| goblin89 wrote:
| MetaSynth is a pretty cool riff on a visual frequency-based DAW.
| (Paid, macOS only.)
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