I’m aware of that you can filter noise to make certain frequencies more prevailing than others. But I would really like to hear (and work with) what a truly gaussian noise-color might sound like!

All suggestions are very welcome!

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1 – that’s not a very accurate transform to a gaussian distribution. To be honest, I not convinced it can really be described as gaussian in any strict sense – it’s just a rough approximation.

A much better method is:

http://en.wikipedia.org/wiki/Box–Muller_transform

2 – Although the *amplitude* distribution of Gaussian noise is by definition Gaussian, that doesn’t have any bearing on its correlation (which may be of different types). If you do not correlate the noise, then it will be white noise, and therefore uncoloured. In this regard it might not be worth your while investigating this, as perceptually the result is very similar to white noise with a flat amplitude distribution.

http://en.wikipedia.org/wiki/Gaussian_noise

HTH

Alex

]]>I guess I was a bit fast on that one – I’m just obsessed with distributions these days… a whole new territory to explore. I bet I have to research it further… But thanx again!

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As to the original question: you could generate Gaussian noise by summing an infinite number of uniform noise sources. But that would cost a lot more CPU.-|

FWIW, one widely-used statistics package approximated a Gaussian distribution by summing a dozen uniform derivates. That’s about as costly as Box-Muller, just less accurate. It does, however, have the [possible] advantage that real “outliers” (extreme values) will never be generated. The extreme values that can occasionally be generated will play havoc with some MSP patches.

In general, you don’t really want to “listen” to Gaussian noise. Subjectively it can hardly be distinguished from flat white noise. Its primary *raison d’être* is for things like dithering.