to be
zero-delay, you can apply it willy-nilly in decision feedback decoding,
on the coding side. And since it's guaranteed to be optimal on the LTI
side of things, and it's a fully neutral, general, and provably
efficient LTI-DSP primitive, why not take advantage of it...? ;)
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dominance for good.
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erms after remodulation.
Those terms will have infinite degree, because of the 1/f term in both
time and frequency, after remodulation. This makes the optimum solution
hard to find, even under the 1/f, 1/t symmetry we already know the
continuous statistics of the problem dictate.
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nfunctions, harmonical analysis, and
the rest of the cool stuff.
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es between
devices.
If you're only doing comparisons, you don't need that.
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it needn't
distort any nonlinear measurement such as THD, per se.
Is there an app for that already?
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they have the channels in order, or with half of them frequency
inverted? What could they be used for?
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of "envelope" are you working with, Eric?
Finally, there are other meanings. The psychoacoustical ones in
particular. Which I'm not too well versed with.
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o what you originally had, even if it converged to *something*
good. :)
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e anything specialized towards sound, but in what
career I did once have, I've modelled my fair share of data within the
relational framework. If you need help in that regard, I'm available.
Enthusiastic, even. :)
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anymore. Certainly not music.
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hase stuff, and then take note of how rapidly your
interpolands grow, for an approximation of what you should do wrt
amplitude.
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ink noise,
yes. But in a discrete time process you have to factor in aliasing. It
goes pretty bad, pretty fast.
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d jump into the fray pretty late. :)
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on
that. Because that way our DSP-sport ain't just about abject numbers
anymore; it has something to do with the real world as well. Its
numerics, its physical acoustical counterparts, and whatnot. :)
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pleasing? If so, maybe we should actually speak
more about how we hear and feel, in respect to our algorithms? And not
so much about how to make an acoustical simulation just right? ;)
(And yes, sorry again, I have a tendency to get carried of a bit. No
harm, no foul, right...)
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long, even with fast
convolution, seems to me to be outa reach of a modern laptop or
desktop PC to do in real time.
No shit! :D
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transforms, because doing it
in silico saves so much power/battery. For that reason there's hardly an
appliance out there with an audio or video output which doesn't have
some Fourier minded machinery imprinted on its very hardware.
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) papers on such architectures.
Obviously, that stuff is pretty specialised and totally irrelevant to
audio DSP. But every now and then, sure, even something as insane as
this really *is* done. ;)
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of samples long
that way in real time, as opposed to perhaps thousands or tens of
thousands in the direct form. So if you can at *any* cost avoid the
direct calculation, you would. ;)
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.
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, 0, 0, 0... Just phase
shift the original sine at the Nyquist frequence.
That'll show you that that precise signal cannot be reconstructed
without resorting to complex continuation of the signal, on the Fourier
plane.
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+358
necessarily hurt, but here it isn't guaranteed to do any good
either. And it's close to doing something bad instead.
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order, and if you do something like 4th to 5th order with 2x
oversampling, it's essentially perfect.
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music-dsp
the dither stream available for subtraction.
You can imagine why I've been playing with this stuff of late.
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Shannon's framework.
i didn't know that. it appears to me to be different, almost
orthogonal to the Shannon thing.
It does, but only because practical implementations can't work with the
bigger than universe codebooks and Markov chains Shannon's mathematical
analysis admits.
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-19a in
data compression theory. I even feature in the oldskool comp.compression
FAQ wrt Burrows-Wheeler Transformation, as the first guy who explained
its compression half to the wider net audience.
Remind me, when were you born again? ;)
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of the most high minded I've
*ever* seen in the field -- actually backs down to something besides the
ideal. Because it's not just audio theory, it's about practicable
solutions as well.
I think you ought to be thinking about the same for a change. ;)
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at
Aalto. It's entirely predicated on this sort of thing in its reverb leg.
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straight
into formulae and simulations. That sort of thing of course sidetracks
you from really getting the wider picture...and as in here, particularly
the edge cases like periodicity and such. :)
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of the discussion, in *musical* DSP? It
sounds like crap, move along, nothing to hear here.
or, paraphrasing Duke Ellington, if it sounds like crap, it IS crap.
Oh, *yes*. :)
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? In addition to and in separation
with the amplitude/modulus side? Because it's rather different, and
might help explain a couple of things in addition to what we've talked
about inb4. :)
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and behaves nastily under change
of parameters.
So then isn't that the end of the discussion, in *musical* DSP? It
sounds like crap, move along, nothing to hear here.
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it that
complicated?
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help it, been looking at a lot of stand up comedy of
late...)
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that growth rates upto polynomial don't matter at all.
Of course some funky global, dual shit happens then: you actually need
all of the samples from -inf to +inf in order to define any polynomial,
and no finitely supported in time subset will suffice.
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. :)
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-yadda, it's what them chips do all the time
for us. Right? ;)
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* that shit. Cool sister bedamned. ;)
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duality
works as it should, and via the Schwartz Representation Theorem captures
the asymptotic growth of tempered distributions. But how you'd utilize
that in DSP or with its oversampling problems is thus far beyond me.
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+358-40
in the
distributional setting, with its functionals and only a local sense of
integration. Trying to interpret something like that the way we do in
conventional L_2 theory sounds likely to lead to pain.
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reasonably sure it is you who is humiliating himself. But of course
I'm open to being proved wrong. Why not do the test?
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of a sequence of
windowed sinc convolutions.
The trouble is that once you go with such a local description, you start
to introduce elements of shift-variance. That sort of thing
automatically breaks down most of the nice structure we have with more
conventional Fourier transforms.
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individual special functions, like FM,
usually nonlinear. But even those have standard texts which deal with
their approximation theory within the bandlimited framework.
If you aim to get things done, as I think an engineers usually does, why
the hell bother with more?
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distributions I mentioned above.
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summed
in by the fact that you only used a real transform, to and fro. (I'm
nowhere sure though.)
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, something
must have gone wrong, where is it.
But yeah, you ought to know about this stuff already. Theo at the very
least.
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might have similar ideas about language, but should it be that they
get judged on the basis of how well they can mimic a certain language
style, or on the content of which they have to say ?
Sometimes, in fact yes. Most of the time, of course not. It depends.
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the Gaussian distribution to the von Mises one, and
then learn to deal with the fact that when you live on a circle,
concepts like phase and its continuity become a bit harder to deal with.
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transformation, just take the DCT
and Walsh bases. Not only do the common cases translate differently
under those bases, the whole ensemble does, too. Because the
Walsh-Hadamard basis doesn't respect time/shift translation symmetry,
like the more common Fourier bases by definition do.
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, and then if you just chose a
minority, nice basis, you should be able to found that choice on
something. So why is it that you seem to found your analysis on a
Fourier basis? E.g. Gabor derived complete bases do have some theory
behind them as well, as do overcomplete time-frequency bases.
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examples
I use, they will come together at undergrad level just fine!
Oh, but you do, and we'd fuck up your thesis instructor as well.
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the precise same fundamental
framework. Seriously, there is no difference.
It's just that I and many others find that framework unduly limiting.
Especially for any analysis involving noise.
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?
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. Even saying I dunno in return is well
enough. Saying tell me more is even better. And if you then tell us
how you did it better than we thought was possible...well then now
*you* are the Master. Don't underestimate the old master, though... ;)
)
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, for computational
*complexity* reasons; cf. Greg Chaitin too, if you want to really fuck
mind up in descriptive complexity ;) ).
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basic framework
*just* that little bit better within the discourse. :) )
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? If so, howso?
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then we can talk clearly about the *much* harder topic that is
perceptual information theory. :)
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of things here already.
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you're done, also
do it under noise (a much nastier proposition, I can tell you).
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researchers at cryptography -list taught me well better in just a couple
of messages.
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theory textbook
I already linked for you, shut up for a little while, read it, do all of
the homework, and then come back.
It's not just that you annoy folks a bit. It's also that you'll never
learn otherwise. It's for your own good, boy. ;)
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source which always
changes state has the maximum entropy?
010101010101010101010101...
...is always maximally informative to the receiver, even if that was the
only message possible? With probability 1?
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it was
generated. Where is the information, from *your* viewpoint?
Maybe I'm one of those cryptographers you're afraid of ;)
That remains to be seen. With 1/100 probability.
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no more
or less information was passed; your context is the same.
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been celebrated as a mathematician who quantified
information once and for all. Not you.
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On 2014-10-12, Peter S wrote:
Rather, please go and read some cryptography papers about entropy
estimation. Then come back, and we can talk further.
PLONK.
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and phase offset.
Exactly. Plus of course if you allow mixtures as well, then you're into
the same territory that Fourier analysis covers, except that your
transform is much less well behaved.
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of course brings us back to your very point: Peter really should
understand the basics of information theory before applying it.
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, okay, not perhaps in the foundational papers, but in pretty much
everything that is worth writing about and interesting enough to be
read. That skews your thinking and especially your vocabulary rather a
bit, and makes you less than an ideal pedagogue.
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of Corti as a whole piece of a closed loop
control system whose overall function is *still* not exhaustively
understood.
That's pretty far from how we typically understand filterbanks, so one
shouldn't take the analogy too far.
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upon
sampling gives you a noise tolrant estimate of what should be subracted.
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of conditions, e.g. using
just three successive samples as input, and those kinds of solutions are
actually in use.
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-computations not only take memory bandwidth
but the kind of low latency which just isn't there.
That's why we're seeing this kind of technology in offline plugins, but
the musicians still play analog instruments. Which of course have zero
latency. ;)
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On 2014-06-19, Rohit Agarwal wrote:
I'm surprised by that statement quite honestly. At a tempo of 200 bpm,
this latency would be roughly 10% of the beat interval which seems to
me quite small.
Then you obviously don't know techno. ;)
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operations just don't feed back to the
operands, like they used to.
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, or the splice point can be
heard.
So, Theo, no analog processing here.
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high
frequencies is ostensibly not linear behavior.
Of course. But then, this kind of a test shows that that human hearing
has even a further, LTI-kinda-looking aspect, which doesn't have to do
with those curves either.
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+358
. After that it ain't noise anymore.
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in a real-time
situation, and wanted to compensate the small (few dBs here and there)
the frequency sensitivity unevenness of the microphone I used, and
applied some jack/jack-rack/ladspa Linux filters for that. Worked
great.
But did it actually constitute a digital wire?
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Sampo Syreeni, aka decoy
subject their loved ones to the test.
Because, I mean, at least for me this was a total mindfuck, and if you
analyze it e.g. via the usual LTI theory of human hearing, the results
do not make any sense at all. I think, but I'm not too sure. Whence
the question. ;)
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Sampo Syreeni, aka decoy - de
of thing is nigh-nonexistent, as
are the patents.
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you don't anticipate
it (this happens in audio editing programs), no volume change, etc. ?
Rather often. It's even an idiom since times immemorial: the bypass
switch.
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as slow,
deleterious and over all editable as well. Kind of like a million
giraffes very close to each other You can't claim it wouldn't be cool,
but...
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not such a problem
because the aesthetics favour lively halls and steady state content, but
anything electronic or percussive, with lots of sharp transients and a
wide bass extension simply turns to mush.
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, in a dependable manner and using clean code.
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critical of Theo's
and other audiophile minded folks' claims over things like ultrahigh
fidelity formats, I must say their understanding of traditional analog
EE and background in tinkering with it probably make them better armed
to deal with this side of the field than I'll ever become.
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of the Delta). They just work and make your life *much*
easier than you ever had it with plain old functions.
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.
For there lies salvation, and the app which pays your bills.
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, and surprisingly often just grant
that you're for now at your wit's end.
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in the percussive section. Of course
some bright fella then also went ahead and invented a speaker coupled
right downto static pressure, presumably just to piss the rest of us
off: https://en.wikipedia.org/wiki/Rotary_woofer .
I should also go kill myself just about now.
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Sampo Syreeni, aka decoy
not to use dither and noise
shaping. I’ll be putting up some additional test files in an article
on ear level.com in the next day or so.
In any case, thank you kindly. Dithering and noise shaping, both in
theory and in practice is *still* something far too few people grasp for
real.
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Sampo
magnitude rolloff. The call's out on such things, nobody
really knows if there's any point to them.
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.
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http
.
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http
hope this helps at least a bit.
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. Pretty sure that
literature has to contain the relevant algorithms if used with just a
single resonance.
Or then just go with second order linear prediction from the impulse
response, and transform back to biquad coefficient space.
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, the end result
at least to me just seems hazier than the original.
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while the area under the negative
and positive halfcycles is still equal (that's one way to approach a
delta/an impulse). In all cases you'll probably be using a leaky
integrator, so getting rid of the DC, because otherwise you risk blowing
up the integrator state.
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