TITLE:: FluidLoudness
SUMMARY:: A Selection of Pitch Descriptors in Real-Time
CATEGORIES:: Libraries>FluidDecomposition
RELATED:: Guides/FluCoMa, Guides/FluidDecomposition, Classes/Pitch

DESCRIPTION::
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).::

	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.

CLASSMETHODS::

METHOD:: kr
	The audio rate in, control rate out version of the object.

ARGUMENT:: in
	The audio to be processed.

ARGUMENT:: kWeighting
(describe argument here)

ARGUMENT:: truePeak
(describe argument here)

ARGUMENT:: winSize
(describe argument here)

ARGUMENT:: hopSize
(describe argument here)

ARGUMENT:: maxWinSize
(describe argument here)

RETURNS::
	A 2-channel KR signal with the [pitch, confidence] descriptors. The latency is winSize.


EXAMPLES::


code::
//create a monitoring bus for the descriptors
b = Bus.new(\control,0,4);

//create a monitoring window for the values
(
w = Window("Loudness Monitor", Rect(10, 10, 220, 115)).front;

c = Array.fill(4, {arg i; StaticText(w, Rect(10, i * 25 + 10, 135, 20)).background_(Color.grey(0.7)).align_(\right)});
c[0].string = ("Loudness: ");
c[1].string = ("Peak: ");
c[2].string = ("Loudness: ");
c[3].string = ("Peak: ");

a = Array.fill(4, {arg i;
	StaticText(w, Rect(150, i * 25 + 10, 60, 20)).background_(Color.grey(0.7)).align_(\center);
});
)

//routine to update the parameters
(
r = Routine {
	{

		b.get({ arg val;
			{
				if(w.isClosed.not) {
					val.do({arg item,index;
						a[index].string = item.round(0.01)})
				}
			}.defer
		});

		0.01.wait;
	}.loop

}.play
)

//test signals, all in one synth
(
x = {
	arg freq=220, type = 1, noise = 0;
			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)}))]);
	Out.kr(b, FluidLoudness.kr(source,winSize:17640,hopSize:4410,maxWinSize:17640) ++ FluidLoudness.kr(source,winSize:132300,hopSize:4410,maxWinSize:132300));
			// Out.kr(b, FluidLoudness.kr(source));
	source.dup;
}.play;
)

// the built-in is slightly better on pure sinewaves
x.set(\freq, 440)

// adding harmonics, by changing to triangle (1), saw (2) or square (3) shows that spectral algo are more resilient when signal are richer
x.set(\type, 1)
x.set(\type, 2)
x.set(\type, 3)

// adding noise shows the comparative sturdiness of the spectral pitch tracker
x.set(\noise, 0.05)

//if latency is no issue, getting a higher winSize will stabilise the algorithm even more
::