Saturation Models (Neve, Tweaker, Oxford Inflator) in FAUST

Mighty23

I’m a simple person—I like loudness!

I’ve been experimenting with an inflator-like idea these past few days, and here’s the code I came up with:

declare name "Inflator-like test1)";

import("stdfaust.lib");

// Interface
input_slider = hslider("Input (dB)", 0, -6, 12, 0.01);
effect_slider = hslider("Effect (%)", 0, 0, 100, 0.1);
curve_slider = hslider("Curve", 0, -50, 50, 0.1);
clip_slider = checkbox("Clip 0 dB");
band_split_slider = checkbox("Band Split");
effect_in_slider = checkbox("Effect In");
output_slider = hslider("Output (dB)", 0, -12, 0, 0.01);

// Utility functions
clamp(x, minv, maxv) = min(maxv, max(minv, x));
sign(x) = (x > 0) - (x < 0);

// Convert UI values to processing parameters
pre = input_slider : ba.db2linear;
post = output_slider : ba.db2linear;
wet = effect_slider * 0.01 * 0.99999955296;
dry = 1 - (effect_slider * 0.01);

// Curve coefficients
A = (curve_slider * 0.01 + 1.5);
B = (curve_slider * -0.02);
C = (curve_slider * 0.01 - 0.5);
D = (0.0625 - curve_slider * 0.0025 + (curve_slider * curve_slider) * 0.000025);

// SVF Filter implementation
svf_coefficient(cutoff) = tan(ma.PI * (cutoff/ma.SR - 0.25)) * 0.5 + 0.5;

svf_lpf(cutoff) = _ : fi.tf2(b0, b1, b2, a1, a2)
with {
    c = svf_coefficient(cutoff);
    b0 = c * c;
    b1 = 2 * b0;
    b2 = b0;
    a1 = 2 * (b0 - 1);
    a2 = 1 - 2 * c + b0;
};

svf_hpf(cutoff) = _ : fi.tf2(b0, b1, b2, a1, a2)
with {
    c = svf_coefficient(cutoff);
    b0 = 1;
    b1 = -2;
    b2 = 1;
    a1 = 2 * (c * c - 1);
    a2 = 1 - 2 * c + c * c;
};

// Band splitting with integrated gain calculation
band_split(x) = low, (mid * mid_gain), high
with {
    low_cutoff = 240;
    high_cutoff = 2400;
    
    c_low = svf_coefficient(low_cutoff);
    c_high = svf_coefficient(high_cutoff);
    mid_gain = c_high * (1 - c_low) / (c_high - c_low);
    
    low = x : svf_lpf(low_cutoff);
    high = x : svf_hpf(high_cutoff);
    mid = x - low - high;
};

// Waveshaping function
waveshaper(x) = select2(abs(x) < 1,
                       select2(abs(x) < 2,
                              x,  // > 2
                              (2 * abs(x) - abs(x) * abs(x)) * sign(x)), // 1 < x < 2
                       (A * abs(x) + B * (abs(x) * abs(x)) + 
                        C * abs(x) * (abs(x) * abs(x)) - 
                        D * ((abs(x) * abs(x)) - 
                            2 * (abs(x) * abs(x)) * abs(x) + 
                            (abs(x) * abs(x) * abs(x) * abs(x)))) * sign(x)) // x < 1
                * wet + x * dry;

// Main processing function
process = par(i, 2, channel_process)
with {
    channel_process(x) = x : input_stage : effect_stage : output_stage;
    
    input_stage(x) = x * pre <: 
                     select2(clip_slider,
                            clamp(_, -2, 2),
                            clamp(_, -1, 1));
    
    effect_stage(x) = select2(effect_in_slider,
                             x,
                             select2(band_split_slider,
                                    waveshaper(x),
                                    band_split_process(x)));
    
    band_split_process(x) = band_split(x) : 
                           (waveshaper, 
                            waveshaper,
                            waveshaper) :> _;
    
    output_stage(x) = select2(abs(x) < 0.0000000000000000555111512312578270211815834045,
                             x * post,
                             0);
};

Morphoice

@Mighty23 looks complicated but sounds nice as far as I can tell. just the band split does nothing

sletz

@Morphoice abs(x) < 0.0000000000000000555111512312578270211815834045

hum seems like a non human intervention here ?

Mighty23

@sletz
Yes, it's not just a non-human intervention—it's a lifesaving one :)

I’ve been dealing with crashes, and after investigating, I realized I need denormal protection. I received several suggestions and implemented the one that made the most intuitive sense to me—though perhaps I’m falling into some cognitive biases?

How can I test for denormal issues?
Can you explain real-world methods for denormal protection?

sletz

@Mighty23

We have tools to help debugging, read:
https://faustdoc.grame.fr/manual/debugging/#debugging-at-runtime
https://faustdoc.grame.fr/tutorials/debugging/

This interp-tracer tool is currently to be used in the terminal, so requires a local installation. But in theory this kind of tool could be integrated in HISE , since the libfaust library used in HISE also embeds the needed Interpreter backend.

For more local NAN protection, using ma.EPSILON is a more portable solution, since is adapts the single/double compilation option.

Allen

@Mighty23
Nice work!
For the band split, you may want to use the linkwitz riley instead of svf.

About the denormal protection, may I know what CPU you're are running this code on? Does this directly cause the crashing?
This might be useful for some legacy CPUs and it won't likely really be a problem for modern x64 CPU afaik.

clevername27

@Mighty23 Very interesting, and thank you for sharing. Am I correct that this is multiband distortion, or is there also some dynamics processing as well?

clevername27

@Morphoice Thank you for sharing. Not a whacking, but I work with two of these companies; could I ask you to pls characterise these as more "…in the spirit of…" and not "…copies of…"?

clevername27

@Allen I assume nobody is here posting actual transfer functions they measured.

Mighty23

@clevername27 said in Saturation Models (Neve, Tweaker, Oxford Inflator) in FAUST:

or is there also some dynamics processing as well?

there is no compressor/limiter/gate in the processing. I would consider it 100% multiband waveshaping.

@Allen said in Saturation Models (Neve, Tweaker, Oxford Inflator) in FAUST:

For the band split, you may want to use the linkwitz riley instead of svf.

Yes, for sure. Many Thanks.

@Allen said in Saturation Models (Neve, Tweaker, Oxford Inflator) in FAUST:

may I know what CPU you're are running this code on?

10510u i7 on Windows
Late 2016 Mini Mac overclocked and open-core operating system.

Morphoice

@clevername27 they're not measured, someone on reddit reverse enginered them so they are somewhat common knowledge among the DSP community, I'm not claiming any of those is a copy of something, it's just knowledge I gathered off the web. This is an old post though, I'm making my own functions for saturation now, and I'm using desmos to create them, closely matching stuff I can indeed measure from real hardware and then bring them over in faust. Unfortunately it's all done by hand, I have no Idea on how to "automatically" transfer measurements into transfer functions. It's a lot of guesswork until the curve looks somewhat similar

Those are the two waveshaper curves I find most pleasing, sonically, anything in between those is great and much faster calculated than the popular tanh for saturation

Screenshot 2025-01-30 at 14.08.55.png

clevername27

@Morphoice Thank you for sharing.

griffinboy

@Morphoice

If you want to find the functions automatically it's not that hard I can show you. It can be done with python or MATLAB very easily.

You'll quicky discover that a static waveshaper cannot represent the measurements of analog distortion, but you can however create the 'best fit' automatically.

Morphoice

@griffinboy it's probably a good idea to morph between waveshaping curves according to signal strength but then again here we are considering hysteresis again ;)

griffinboy

@Morphoice

yeah no, morphing a waveshaper based on signal strength doesn't really have much effect unless it has memory or smoothing. Else you've just done a static transformation of the curve and created another static curve. It has to have memory / hysteresis to actually represent anything nonlinear.

But collecting transfer function data from analog devices can still be super useful and can inform approximations.

Chazrox

@Morphoice Where can I learn how to apply this? Maybe you can tell me what this is and I can do y research. Thanks brotha.

clevername27

This post is deleted!

clevername27

@griffinboy Doing each partial statically will model non-linearity.

Saturation Models (Neve, Tweaker, Oxford Inflator) in FAUST

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