flucoma-sc/release-packaging/Examples/dataset/1-learning examples/11-compositing-datasets.scd

// here we will define a process that creates and populates a series of parallel dataset, one of each 'feature-space' that we can then eventually manipulate more easily than individual dimensions.

// define a few datasets
(
~pitchDS = FluidDataSet(s);
~loudDS = FluidDataSet(s);
~mfccDS = FluidDataSet(s);
~durDS = FluidDataSet(s);

//define as many buffers as we have parallel voices/threads in the extractor processing (default is 4)
~pitchbuf = 4.collect{Buffer.new};
~statsPitchbuf = 4.collect{Buffer.new};
~weightPitchbuf = 4.collect{Buffer.new};
~flatPitchbuf = 4.collect{Buffer.new};
~loudbuf = 4.collect{Buffer.new};
~statsLoudbuf = 4.collect{Buffer.new};
~flatLoudbuf = 4.collect{Buffer.new};
~weightMFCCbuf = 4.collect{Buffer.new};
~mfccbuf = 4.collect{Buffer.new};
~statsMFCCbuf = 4.collect{Buffer.new};
~flatMFCCbuf = 4.collect{Buffer.new};

// here we instantiate a loader as per example 0
~loader = FluidLoadFolder(File.realpath(FluidBufPitch.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/");

// here we instantiate a further slicing step as per example 0
~slicer = FluidSliceCorpus({ |src,start,num,dest|
	FluidBufOnsetSlice.kr(src ,start, num, indices:dest, metric: 9, threshold:0.2, minSliceLength: 17, blocking: 1)
});

// here we make the full processor building our 3 source datasets
~extractor = FluidProcessSlices({|src,start,num,data|
	var label, voice, pitch, pitchweights, pitchstats, pitchflat, loud, statsLoud, flattenLoud, mfcc, mfccweights, mfccstats, mfccflat, writePitch, writeLoud;
	label = data.key;
    voice = data.value[\voice];
	// the pitch computation is independant so it starts right away
	pitch = FluidBufPitch.kr(src, startFrame:start, numFrames:num, numChans:1, features:~pitchbuf[voice], unit: 1, trig:1, blocking: 1);
	pitchweights = FluidBufThresh.kr(~pitchbuf[voice], numChans: 1, startChan: 1, destination: ~weightPitchbuf[voice], threshold: 0.7, trig:Done.kr(pitch), blocking: 1);//pull down low conf
	pitchstats = FluidBufStats.kr(~pitchbuf[voice], stats:~statsPitchbuf[voice], numDerivs: 1, weights: ~weightPitchbuf[voice], outliersCutoff: 1.5, trig:Done.kr(pitchweights), blocking: 1);
	pitchflat = FluidBufFlatten.kr(~statsPitchbuf[voice],destination:~flatPitchbuf[voice],trig:Done.kr(pitchstats),blocking: 1);
	writePitch = FluidDataSetWr.kr(~pitchDS,label, nil, ~flatPitchbuf[voice], Done.kr(pitchflat),blocking: 1);
	// the mfcc need loudness to weigh, so let's start with that
	loud = FluidBufLoudness.kr(src,startFrame:start, numFrames:num, numChans:1, features:~loudbuf[voice], trig:Done.kr(writePitch), blocking: 1);//here trig was 1
	//we can now flatten and write Loudness in its own trigger tree
	statsLoud = FluidBufStats.kr(~loudbuf[voice], stats:~statsLoudbuf[voice], numDerivs: 1, trig:Done.kr(loud), blocking: 1);
	flattenLoud = FluidBufFlatten.kr(~statsLoudbuf[voice],destination:~flatLoudbuf[voice],trig:Done.kr(statsLoud),blocking: 1);
	writeLoud = FluidDataSetWr.kr(~loudDS,label, nil, ~flatLoudbuf[voice], Done.kr(flattenLoud),blocking: 1);
	//we can resume from the loud computation trigger
	mfcc = FluidBufMFCC.kr(src,startFrame:start,numFrames:num,numChans:1,features:~mfccbuf[voice],trig:Done.kr(writeLoud),blocking: 1);//here trig was loud
	mfccweights = FluidBufScale.kr(~loudbuf[voice],numChans: 1,destination: ~weightMFCCbuf[voice],inputLow: -70,inputHigh: 0, trig: Done.kr(mfcc), blocking: 1);
	mfccstats = FluidBufStats.kr(~mfccbuf[voice], stats:~statsMFCCbuf[voice], startChan: 1, numDerivs: 1, weights: ~weightMFCCbuf[voice], trig:Done.kr(mfccweights), blocking: 1);//remove mfcc0 and weigh by loudness instead
	mfccflat = FluidBufFlatten.kr(~statsMFCCbuf[voice],destination:~flatMFCCbuf[voice],trig:Done.kr(mfccstats),blocking: 1);
	FluidDataSetWr.kr(~mfccDS,label, nil, ~flatMFCCbuf[voice], Done.kr(mfccflat),blocking: 1);
});

)
//////////////////////////////////////////////////////////////////////////
//loading process

//load and play to test if it is that quick - it is!
(
t = Main.elapsedTime;
~loader.play(s,action:{(Main.elapsedTime - t).postln;"Loaded".postln;{var start, stop; PlayBuf.ar(~loader.index[~loader.index.keys.asArray.last.asSymbol][\numchans],~loader.buffer,startPos: ~loader.index[~loader.index.keys.asArray.last.asSymbol][\bounds][0])}.play;});
)

//////////////////////////////////////////////////////////////////////////
// slicing process

// run the slicer
(
t = Main.elapsedTime;
~slicer.play(s,~loader.buffer,~loader.index,action:{(Main.elapsedTime - t).postln;"Slicing done".postln});
)
//slice count
~slicer.index.keys.size

//////////////////////////////////////////////////////////////////////////
// description process

// run the descriptor extractor (errors will be given, this is normal: the pitch conditions are quite exacting and therefore many slices are not valid)
(
t = Main.elapsedTime;
~extractor.play(s,~loader.buffer,~slicer.index,action:{(Main.elapsedTime - t).postln;"Features done".postln});
)

// make a dataset of durations for querying that too (it could have been made in the process loop, but hey, we have dictionaries we can manipulate too!)
(
~dict = Dictionary.new;
~temp = ~slicer.index.collect{ |k| [k[\bounds][1] - k[\bounds][0]]};
~dict.add(\data -> ~temp);
~dict.add(\cols -> 1);
~durDS.load(~dict)
)

//////////////////////////////////////////////////////////////////////////
// manipulating and querying the data

~pitchDS.print;
~loudDS.print;
~mfccDS.print;
~durDS.print;

///////////////////////////////////////////////////////
//reduce the MFCC timbral space stats (many potential ways to explore here... - 2 are provided to compare, with and without the derivatives before running a dimension reduction)
~tempDS = FluidDataSet(s);

~query = FluidDataSetQuery(s);
~query.addRange(0,24);//add only means and stddev of the 12 coeffs...
~query.addRange((7*12),24);// and the same stats of the first derivative (moving 7 stats x 12 mfccs to the right)
~query.transform(~mfccDS, ~tempDS);

//check that you end up with the expected 48 dimensions
~tempDS.print;

// standardizing before the PCA, as argued here:
// https://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html
~stan = FluidStandardize(s);
~stanDS = FluidDataSet(s);
~stan.fitTransform(~tempDS,~stanDS)

//shrinking A: using 2 stats on the values, and 2 stats on the redivative (12 x 2 x 2 = 48 dim)
~pca = FluidPCA(s,4);//shrink to 4 dimensions
~timbreDSd = FluidDataSet(s);
~pca.fitTransform(~stanDS,~timbreDSd,{|x|x.postln;})//accuracy

//shrinking B: using only the 2 stats on the values
~query.clear;
~query.addRange(0,24);//add only means and stddev of the 12 coeffs...
~query.transform(~stanDS, ~tempDS);//retrieve the values from the already standardized dataset

//check you have the expected 24 dimensions
~tempDS.print;

//keep its own PCA so we can keep the various states for later transforms
~pca2 = FluidPCA(s,4);//shrink to 4 dimensions
~timbreDS = FluidDataSet(s);
~pca2.fitTransform(~tempDS,~timbreDS,{|x|x.postln;})//accuracy

// comparing NN for fun
~targetDSd = Buffer(s)
~targetDS = Buffer(s)
~tree = FluidKDTree(s,5)

// you can run this a few times to have fun
(
~target = ~slicer.index.keys.asArray.scramble.[0].asSymbol;
~timbreDSd.getPoint(~target, ~targetDSd);
~timbreDS.getPoint(~target, ~targetDS);
)

~tree.fit(~timbreDSd,{~tree.kNearest(~targetDSd,{|x|~nearestDSd = x.postln;})})
~tree.fit(~timbreDS,{~tree.kNearest(~targetDS,{|x|~nearestDS = x.postln;})})

// play them in a row
(
Routine{
5.do{|i|
	var dur;
	v = ~slicer.index[~nearestDSd[i].asSymbol];
	dur = (v[\bounds][1] - v[\bounds][0]) / s.sampleRate;
	{BufRd.ar(v[\numchans],~loader.buffer,Line.ar(v[\bounds][0],v[\bounds][1],dur, doneAction: 2))}.play;
	~nearestDSd[i].postln;
	dur.wait;
	};
}.play;
)

(
Routine{
5.do{|i|
	var dur;
	v = ~slicer.index[~nearestDS[i].asSymbol];
	dur = (v[\bounds][1] - v[\bounds][0]) / s.sampleRate;
	{BufRd.ar(v[\numchans],~loader.buffer,Line.ar(v[\bounds][0],v[\bounds][1],dur, doneAction: 2))}.play;
	~nearestDS[i].postln;
	dur.wait;
	};
}.play;
)

///////////////////////////////////////////////////////
// compositing queries - defining a target and analysing it

~globalDS = FluidDataSet(s);

// define a source
~targetsound = Buffer.read(s,File.realpath(FluidBufPitch.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/Tremblay-ASWINE-ScratchySynth-M.wav",42250,44100);
~targetsound.play

// analyse it as above, using voice 0 in the arrays of buffer to store the info
(
{
	var label, voice, pitch, pitchweights, pitchstats, pitchflat, loud, statsLoud, flattenLoud, mfcc, mfccweights, mfccstats, mfccflat, writePitch, writeLoud;
	pitch = FluidBufPitch.kr(~targetsound, numChans:1, features:~pitchbuf[0], unit: 1, trig:1, blocking: 1);
	pitchweights = FluidBufThresh.kr(~pitchbuf[0], numChans: 1, startChan: 1, destination: ~weightPitchbuf[0], threshold: 0.7, trig:Done.kr(pitch), blocking: 1);
	pitchstats = FluidBufStats.kr(~pitchbuf[0], stats:~statsPitchbuf[0], numDerivs: 1, weights: ~weightPitchbuf[0], outliersCutoff: 1.5, trig:Done.kr(pitchweights), blocking: 1);
	pitchflat = FluidBufFlatten.kr(~statsPitchbuf[0],destination:~flatPitchbuf[0],trig:Done.kr(pitchstats),blocking: 1);
	loud = FluidBufLoudness.kr(~targetsound, numChans:1, features:~loudbuf[0], trig:Done.kr(pitchflat), blocking: 1);
	statsLoud = FluidBufStats.kr(~loudbuf[0], stats:~statsLoudbuf[0], numDerivs: 1, trig:Done.kr(loud), blocking: 1);
	flattenLoud = FluidBufFlatten.kr(~statsLoudbuf[0],destination:~flatLoudbuf[0],trig:Done.kr(statsLoud),blocking: 1);
	mfcc = FluidBufMFCC.kr(~targetsound,numChans:1,features:~mfccbuf[0],trig:Done.kr(flattenLoud),blocking: 1);
	mfccweights = FluidBufScale.kr(~loudbuf[0],numChans: 1,destination: ~weightMFCCbuf[0],inputLow: -70,inputHigh: 0, trig: Done.kr(mfcc), blocking: 1);
	mfccstats = FluidBufStats.kr(~mfccbuf[0], stats:~statsMFCCbuf[0], startChan: 1, numDerivs: 1, weights: ~weightMFCCbuf[0], trig:Done.kr(mfccweights), blocking: 1);
	mfccflat = FluidBufFlatten.kr(~statsMFCCbuf[0],destination:~flatMFCCbuf[0],trig:Done.kr(mfccstats),blocking: 1);
	FreeSelf.kr(Done.kr(mfccflat));
}.play;
)

// a first query - length and pitch
~query.clear
~query.filter(0,"<",44100+22050)//column0 a little smaller than our source
~query.and(0,">", 44100-22050)//also as far as a little larger than the source
~query.transformJoin(~durDS, ~pitchDS, ~tempDS); //this passes to ~tempDS only the points that have the same label than those in ~durDS that satisfy the condition. No column were added so nothing from ~durDS is copied

// print to see how many slices (rows) we have
~tempDS.print

// further conditions to assemble the query
~query.clear
~query.filter(11,">",0.7)//column11 (median of pitch confidence) larger than 0.7
~query.addRange(0,4) //copy only mean and stddev of pitch and confidence
~query.transform(~tempDS, ~globalDS); // pass it to the final search

// print to see that we have less items, with only their pitch
~globalDS.print

// compare knearest on both globalDS and tempDS
// assemble search buffer
~targetPitch = Buffer(s)
FluidBufCompose.process(s, ~flatPitchbuf[0],numFrames: 4,destination: ~targetPitch)

// feed the trees
~tree.fit(~pitchDS,{~tree.kNearest(~flatPitchbuf[0],{|x|~nearestA = x.postln;})}) //all the points with all the stats
~tree.fit(~globalDS,{~tree.kNearest(~targetPitch,{|x|~nearestB = x.postln;})}) //just the points with the right lenght conditions, with the curated stats

// play them in a row
(
Routine{
5.do{|i|
	var dur;
	v = ~slicer.index[~nearestA[i].asSymbol];
	dur = (v[\bounds][1] - v[\bounds][0]) / s.sampleRate;
	{BufRd.ar(v[\numchans],~loader.buffer,Line.ar(v[\bounds][0],v[\bounds][1],dur, doneAction: 2))}.play;
	~nearestA[i].postln;
	dur.wait;
	};
}.play;
)

// with our duration limits, strange results appear eventually
(
Routine{
5.do{|i|
	var dur;
	v = ~slicer.index[~nearestB[i].asSymbol];
	dur = (v[\bounds][1] - v[\bounds][0]) / s.sampleRate;
	{BufRd.ar(v[\numchans],~loader.buffer,Line.ar(v[\bounds][0],v[\bounds][1],dur, doneAction: 2))}.play;
	~nearestB[i].postln;
	dur.wait;
	};
}.play;
)

///////////////////////////////////////////////////////
// compositing queries to weigh - defining a target and analysing it

// make sure to define and describe the source above (lines 178 to 201)

// let's make normalised versions of the 3 datasets, keeping the normalisers separate to query later
~loudDSn = FluidDataSet(s);
~pitchDSn = FluidDataSet(s);
~timbreDSn = FluidDataSet(s);

~normL = FluidNormalize(s)
~normP = FluidNormalize(s)
~normT = FluidNormalize(s)

~normL.fitTransform(~loudDS, ~loudDSn);
~normP.fitTransform(~pitchDS, ~pitchDSn);
~normT.fitTransform(~timbreDSd, ~timbreDSn);

// let's assemble these datasets
~query.clear
~query.addRange(0,4)
~query.transformJoin(~pitchDSn,~timbreDSn, ~tempDS) //appends 4 dims of pitch to 4 dims of timbre
~query.transformJoin(~loudDSn, ~tempDS, ~globalDS) // appends 4 dims of loud to the 8 dims above

~globalDS.print//12 dim: 4 timbre, 4 pitch, 4 loud, all normalised between 0 and 1
~globalDS.write(Platform.defaultTempDir ++ "test12dims.json") // write to file to look at the values
// open the file in your default json editor
(Platform.defaultTempDir ++ "test12dims.json").openOS

// let's assemble the query
// first let's normalise our target descriptors
(
~targetPitch = Buffer(s);
~targetLoud = Buffer(s);
~targetMFCC = Buffer(s);
~targetMFCCs = Buffer(s);
~targetMFCCsp = Buffer(s);
~targetTimbre = Buffer(s);
~targetAll= Buffer(s);
)

~normL.transformPoint(~flatLoudbuf[0], ~targetLoud) //normalise the loudness (all dims)
~normP.transformPoint(~flatPitchbuf[0], ~targetPitch) //normalise the pitch (all dims)
FluidBufCompose.process(s,~flatMFCCbuf[0],numFrames: 24,destination: ~targetMFCC) // copy the process of dimension reduction above
FluidBufCompose.process(s,~flatMFCCbuf[0],startFrame: (7*12), numFrames: 24, destination: ~targetMFCC,destStartFrame: 24) //keeping 48 dims
~stan.transformPoint(~targetMFCC,~targetMFCCs) //standardize with the same coeffs
~pca.transformPoint(~targetMFCCs, ~targetMFCCsp) //then down to 4
~normT.transformPoint(~targetMFCCsp, ~targetTimbre) //then normalised
FluidBufCompose.process(s, ~targetTimbre,destination: ~targetAll) // assembling the single query
FluidBufCompose.process(s, ~targetPitch, numFrames: 4, destination: ~targetAll, destStartFrame: 4) // copying the 4 stats of pitch we care about
FluidBufCompose.process(s, ~targetLoud, numFrames: 4, destination: ~targetAll, destStartFrame: 8) // same for loudness
//check the sanity
~targetAll.query

// now let's see which is nearest that point
~tree.fit(~globalDS,{~tree.kNearest(~targetAll,{|x|~nearest = x.postln;})}) //just the points with the right lenght conditions, with the curated stats

// play them in a row
(
Routine{
5.do{|i|
	var dur;
	v = ~slicer.index[~nearest[i].asSymbol];
	dur = (v[\bounds][1] - v[\bounds][0]) / s.sampleRate;
	{BufRd.ar(v[\numchans],~loader.buffer,Line.ar(v[\bounds][0],v[\bounds][1],dur, doneAction: 2))}.play;
	~nearest[i].postln;
	dur.wait;
	};
}.play;
)

// to change the relative weight of each dataset, let's change the normalisation range. Larger ranges will mean larger distance, and therefore less importance for that parameter.
// for instance to downplay pitch, let's make it larger by a factor of 10 around the center of 0.5
~normP.max = 5.5
~normP.min = -4.5
~normP.fitTransform(~pitchDS, ~pitchDSn);
// here we can re-run just the part that composites the pitch
~normP.transformPoint(~flatPitchbuf[0], ~targetPitch) //normalise the pitch (all dims)
FluidBufCompose.process(s, ~targetPitch, numFrames: 4, destination: ~targetAll, destStartFrame: 4) // copying the 4 stats of pitch we care about

//see that the middle 4 values are much larger in range
~targetAll.getn(0,12,{|x|x.postln;})

// let's re-assemble these datasets
~query.transformJoin(~pitchDSn,~timbreDSn, ~tempDS) //appends 4 dims of pitch to 4 dims of timbre
~query.transformJoin(~loudDSn, ~tempDS, ~globalDS) // appends 4 dims of loud to the 8 dims above

// now let's see which is nearest that point
~tree.fit(~globalDS,{~tree.kNearest(~targetAll,{|x|~nearest = x.postln;})}) //just the points with the right lenght conditions, with the curated stats

///////////////////////////////////////////////
// todo: segment then query musaik