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83 lines
2.5 KiB
Markdown
83 lines
2.5 KiB
Markdown
s.reboot;
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//Preliminaries: we want some audio, a couple of FluidDataSets, some Buffers
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(
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~raw = FluidDataSet(s,\Mel40);
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~norm = FluidDataSet(s,\Mel40n);
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~retrieved = FluidDataSet(s,\ae2);
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~audio = Buffer.read(s,File.realpath(FluidBufMelBands.class.filenameSymbol).dirname +/+ "../AudioFiles/Tremblay-ASWINE-ScratchySynth-M.wav");
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~melfeatures = Buffer.new(s);
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~stats = Buffer.alloc(s, 7, 40);
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~datapoint = Buffer.alloc(s, 40);
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~mlp = FluidMLPRegressor(s,[9,2,9],activation: 1,outputActivation: 1,tapIn: 0,tapOut: 2,maxIter: 10000,learnRate: 0.1,momentum: 0.1,batchSize: 10,validation: 0.1);
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~normalizer = FluidNormalize(s);
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)
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// process the melbands
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FluidBufMelBands.process(s,~audio, features: ~melfeatures,action: {\done.postln;});
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// Divide the time series in 100, and take the mean of each segment and add this as a point to
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// the 'raw' FluidDataSet
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(
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{
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var trig = LocalIn.kr(1, 1);
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var buf = LocalBuf(40, 1);
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var count = PulseCount.kr(trig) - 1;
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var chunkLen = (~melfeatures.numFrames / 100).asInteger;
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var stats = FluidBufStats.kr(source: ~melfeatures, startFrame: count * chunkLen, numFrames: chunkLen, stats: ~stats, trig: trig);
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var rd = BufRd.kr(40, ~stats, DC.kr(0), 0, 1);
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var bufWr, dsWr;
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40.do{|i|
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bufWr = BufWr.kr(rd[i], buf, DC.kr(i));
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};
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dsWr = FluidDataSetWr.kr(\MLP40, buf: buf, trig: Done.kr(stats));
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LocalOut.kr( Done.kr(dsWr));
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FreeSelf.kr(count - 99);
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Poll.kr(trig,count);
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}.play;
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)
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// wait for the post window to acknoledge the job is done.
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// normalize the input
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~normalizer.fitTransform(~raw,~norm);
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//we can then run the AE - the server might become yellow :)
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~mlp.fit(~norm,~norm,{|x|x.postln;});
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//we can then retrieve the hidden layer #2
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~mlp.predict(~norm,~retrieved)
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//check the structure of retrieved
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~retrieved.print
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//let's normalise it for display
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~normData = FluidDataSet(s,\ae2N);
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~reducedarray = Array.new(100);
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~normalView = FluidNormalize(s);
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(
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~normalView.fitTransform(~retrieved,~normData, action:{
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~normData.dump{|x| 100.do{|i|
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~reducedarray.add(x["data"][i.asString])
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}};
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});
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)
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~normData.print
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~reducedarray.postln;
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//Visualise the 2D projection of our original 12D data
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(
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d = ~reducedarray.flatten(1).unlace.deepCollect(1, { |x| x.normalize});
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w = Window("scatter", Rect(128, 64, 200, 200));
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w.drawFunc = {
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Pen.use {
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d[0].size.do{|i|
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var x = (d[0][i]*200);
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var y = (d[1][i]*200);
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var r = Rect(x,y,5,5);
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Pen.fillColor = Color.blue;
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Pen.fillOval(r);
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}
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}
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};
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w.refresh;
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w.front;
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) |