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TITLE:: FluidLoudness
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SUMMARY:: A Selection of Pitch Descriptors in Real-Time
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CATEGORIES:: Libraries>FluidDecomposition
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RELATED:: Guides/FluCoMa, Guides/FluidDecomposition, Classes/Pitch
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DESCRIPTION::
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This class implements three popular pitch descriptors, computed as frequency and the confidence in its value. It is part of the Fluid Decomposition Toolkit of the FluCoMa project.FOOTNOTE:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
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The process will return a multichannel control steam with [pitch, confidence] values, which will be repeated if no change happens within the algorithm, i.e. when the hopSize is larger than the server's kr period.
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CLASSMETHODS::
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METHOD:: kr
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The audio rate in, control rate out version of the object.
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ARGUMENT:: in
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The audio to be processed.
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ARGUMENT:: kWeighting
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(describe argument here)
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ARGUMENT:: truePeak
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(describe argument here)
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ARGUMENT:: winSize
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(describe argument here)
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ARGUMENT:: hopSize
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(describe argument here)
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ARGUMENT:: maxWinSize
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(describe argument here)
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RETURNS::
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A 2-channel KR signal with the [pitch, confidence] descriptors. The latency is winSize.
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EXAMPLES::
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code::
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//create a monitoring bus for the descriptors
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b = Bus.new(\control,0,4);
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//create a monitoring window for the values
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(
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w = Window("Loudness Monitor", Rect(10, 10, 220, 115)).front;
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c = Array.fill(4, {arg i; StaticText(w, Rect(10, i * 25 + 10, 135, 20)).background_(Color.grey(0.7)).align_(\right)});
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c[0].string = ("Loudness: ");
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c[1].string = ("Peak: ");
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c[2].string = ("Loudness: ");
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c[3].string = ("Peak: ");
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a = Array.fill(4, {arg i;
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StaticText(w, Rect(150, i * 25 + 10, 60, 20)).background_(Color.grey(0.7)).align_(\center);
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});
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)
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//routine to update the parameters
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(
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r = Routine {
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{
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b.get({ arg val;
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{
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if(w.isClosed.not) {
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val.do({arg item,index;
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a[index].string = item.round(0.01)})
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}
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}.defer
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});
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0.01.wait;
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}.loop
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}.play
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)
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//test signals, all in one synth
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(
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x = {
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arg freq=220, type = 1, noise = 0;
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var source = PinkNoise.ar(noise) + Select.ar(type,[DC.ar(),SinOsc.ar(freq,mul:0.1), VarSaw.ar(freq,mul:0.1), Saw.ar(freq,0.1), Pulse.ar(freq,mul:0.1), Mix.new(Array.fill(8, {arg i; SinOsc.ar(LFNoise1.kr(0.1.rand,10,220*(i+1)),mul:(i+1).reciprocal * 0.1)}))]);
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Out.kr(b, FluidLoudness.kr(source,winSize:17640,hopSize:4410,maxWinSize:17640) ++ FluidLoudness.kr(source,winSize:132300,hopSize:4410,maxWinSize:132300));
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// Out.kr(b, FluidLoudness.kr(source));
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source.dup;
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}.play;
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)
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// the built-in is slightly better on pure sinewaves
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x.set(\freq, 440)
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// adding harmonics, by changing to triangle (1), saw (2) or square (3) shows that spectral algo are more resilient when signal are richer
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x.set(\type, 1)
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x.set(\type, 2)
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x.set(\type, 3)
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// adding noise shows the comparative sturdiness of the spectral pitch tracker
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x.set(\noise, 0.05)
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//if latency is no issue, getting a higher winSize will stabilise the algorithm even more
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::
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