Anti-derivative Anti-aliasing (ADAA) issues

General JUCE discussion
1

Hi all DSP experts!

I am trying yo reduce the aliasing of four cascaded nonlinear saturating/wavefolding functions and I would like to do some questions.

For each nonlinear block, I am applying a first order antiderivative antialiasing method, I make my prototype in MATLAB and it works fine. However, when I go towards C++/JUCE implementation, I need to make higher my ‘tolerance’ value (the tolerance value is the value that controls the ill condition of the ADAA) to not to listen to strange artifacts. Moreover, I need to reduce also the input gain/trim range in order to not get NaN values. These issues do not come out without ADAA, even if I get more aliasing.
Do these problems arise because of float single-precision number representation?

Moreover, each nonlinear block with ADAA introduces a latency of 0.5 samples. Since I am applying oversampling before the nonlinear process, should I do average my latency with the oversampling rate?
In other words, my total latency is
totLatencySamples = oversamplerLatency + 0.5*numberOfNLProcess
or
totLatencySamples = oversamplerLatency + 0.5*numberOfNLProcess*baseSamplingRate/oversampSamplingRate?

Thank you very much!

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2

Most likely, and it depends on the waveshaper -some integrals are pretty intolerant to low precision.

This one.

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3

Thank you very much @kamedin for you brilliant and concise reply! :slight_smile:

So could I cast the input to double precision and then apply ADAA and then come back to float?

4

Yup, making the waveshapers work on doubles would most likely help. Also, test it with different inputs and take a look at a spectral view, because even if the ugly loud noise is gone, you may still have a haze of high frequency noise that’s not nice either.

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5

Yes, it is is exactly what I got with my implementation, when I talked about ‘artifacts’

Up to now, with an high tolerance value (tol = 0.001) and a limited input gain range, it works quite good in single-precision. But it still does not seem ‘robust’

6

The math of the thing is complicated, it’s not easy to predict. You’ll have to test on a reasonably wide range of inputs and call it a day at some point. If it doesn’t give a visible hf haze in your tests, you can trust that at least it won’t go NaN.

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