more (most of this is nit-picking, though some mistakes are fundamental errors)
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It's not particularly intuitive: the concept of adding noise to reduce noise doesn't make a lot of sense.
You don't reduce noise. You reduce distortion. You increase noise.
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\"Dither\" is a British colloquialism for \"vibration\".
To my knowledge, it isn't, but I could be wrong. To dither is to be indecisive - to not be able to make your mind up. To be "in a dither" is to be confused about the appropriate course of action. The nearest it gets to "vibration" is the idea of people moving between the two possibilities (e.g. should I go left or right? I'll go left <starts to go left> - no, I'll go right <starts go to right>).
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there are a bunch of small square waves of determinable size (based on the frequency of the original waveform).
they're steps, not square waves. (told you this was nit picking). The amplitude (height) of each step is equal to the step-size of the quantiser, and the duration (length) of each step is dependent on the slope of the original waveform, which arises due to its amplitude and frequency.
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If this were a \"fully maximized\" signal these peaks would be much lower.
I prefer "one that reaches digital full scale" to "fully maximized". The distortion peaks will be lower for a higher amplitude signal. The reason is because there's the same amount of quantisation error
whatever the signal, but it's signal correlated (i.e. harmonic distortion) for a low level signal, but semi-decorrelated for a high level signal (i.e. it appears as broadband noise).
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Now what we're trying to simulate is real white noise.
No it's not! Where did you get this from? The idea persists in the whole discussion that what we really want is nice analogue gaussian noise, but TPDF dither will do. This is rubbish!
What we want to do is linearise the quantiser: in English, the quantiser has a step function - anything below a certain level falls into one numerical bin, anything above it falls into the next bin. If you plot an input output function, you get a staircase. We don't want this - we want to remove the steps, and have a nice straight line. We want the graph y=x - it means what you put in is what you get out. Perfect!
We can't get a straight line, but
if we add the right about of noise, what happens is that a signal 20% above the limit between two quantisation steps has a 80% chance of falling on the lower step, and a 20% chance of falling on the upper step. A signal half way between the two steps has a 50% chance of falling on either.
Because of the noise, any given signal between two steps could now fall on either step (as opposed to always falling on the lower one - the case without dither, if you round down) - but the chance of it falling on either step is proportional to the actual level of the signal. So, if you average the signal, the steps disappear! Luckily, our ears do perform a kind of averaging - if you look in the frequency domain (which our ears, and all these pretty spectrograms do) you have to average in the time domain - you average over many many samples, and the stair case evens out.
So far, so good. This is using RECTANGULAR pdf noise. The problem is that the staircase is linear (there's no harmonic distortion) BUT signals half way between the two steps are never moved by more than 50% of a step, whereas signals at other locations can be moved further - up to 99.9% in fact. So the amount a signal gets shifted is dependent on its amplitude. This gives rise to noise modulation - it's more subtle than harmonic distortion, but it can be audible. This is solved by adding another RECTANGULAR pdf noise signal. The two together give rise to a TRIANGULAR noise signal. This sometimes pushes the signal over TWO steps, and (though I really can't explain it without a diagram!) means that signals of any amplitude are, on average, shifted by the same amount.
And this is ideal. no distortion, because the steps have been linearised, and no noise modulation, because the same amount of noise is added to any amplitude signal. So that's the first and second order problems of the quantiser fixed. There must be third, fourth, fifth etc problems too, but whereas you could quantify them using a mathematical analysis, your ear can't hear them, so we stop here!
So triangular dither is ideal. NOT gaussian - gaussian DOES linearise the quantiser, but it doesn't quite remove the noise modulation. The amount of noise modulation you have left is very small, but it's not zero. So analogue Gaussian dither (at the ADC) will do nicely, but in the digital domain there's no advantage to Gaussian dither at all.
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This is called \"square probability density function\".
It's called a rectangular PDF - I've not heard anyone call it square before, but I've probably not read the correct textbooks.
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this is called \"Triangular Probability\" or TPDF. TRUE white noise, however...
Hang on - if you use those dice (or a rect. or tri. PDF random number generator) to drive a digital to analogue converter, you WILL get white noise, up to fs/2. "TRUE white noise" is noise with a long term flat spectrum. Those dice are giving you TRUE white noise. The idea that Gaussian is TRUE white noise and triangular isn't TRUE white noise is complete nonsense.
Noise shaping - really bad explanation. And whether you call the UV22 system "dither" or not (I wouldn't, but then I like to call ideal dither "dither", and anything else "non-ideal noise" ;-) ),
the UV22 system does not use noise shaping (according to their publication, though they could be hiding the truth for commercial reasons).
UV22 adds high frequency noise. Noise shaped systems don't have to add any noise, but what they do have to do (to be called noise shaping) is take the output, and feed part of it back to the input - this negative feedback system, with appropriate filtering in the negative feedback path, will push any quantisation noise and/or dither to higher frequencies. The UV22 system doesn't do any "pushing" - it just puts the noise there to start with and hopes. (repeat: according to their publication, though they could be hiding the truth for commercial reasons).
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One last note is that the benefits of dither are more apparent on lower frequency material than higher frequency material. On higher frequency material the artifacts created by quantizing error can be above the hearing range and therefore not as detrimental to the listening process.
It's true that lower frequencies give rise to quantisation distortion that is highly correlated with the signal, and hence the resulting harmonic distortion is louder - or the quantisation error is more tone-like and less noise-like. But (inaudible) pure high frequency signals can give rise to large amounts of in-band quantisation distortion. Try a -30dB 20kHz sine wave in cool edit pro, and convert it to 8-bit without dither - you can hear the distortion tone! Use dither, and it goes away.
The thing is, the quantising error doesn't have an anti-alias filter applied to it - so any harmonic components that should have been above the nyquist limit of fs/2 will be folded back down into the audible range.
I can't find any more errors. If I do contact the author, I'll send him a copy of the best papers by Lipshitz and Vanderkooy. The maths can be hard to follow, but the explanations are great.
Cheers,
David.
http://www.David.Robinson.org/