removed the object finder and the pretrained piano examples from nmfmatch help and put it in the example folder
Pierre Alexandre Tremblay
6 years ago
parent
ca34201054
commit
e407c2efb8
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// using NMF, splitting a small portion, then associating components to targets, then thresholding on these target's activations to find objects.
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//set some buffers
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(
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b = Buffer.read(s,File.realpath(FluidNMFMatch.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/Tremblay-BaB-SoundscapeGolcarWithDog.wav");
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c = Buffer.new(s);
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x = Buffer.new(s);
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e = Buffer.new(s);
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)
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// train where all objects are present
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(
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Routine {
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FluidBufNMF.process(s,b,130000,150000,0,1, c, x, components:10);
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c.query;
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}.play;
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)
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// wait for the query to print
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// then find a component for each item you want to find. You could also sum them. Try to find a component with a good object-to-rest ratio
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(
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~dog =1;
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{PlayBuf.ar(10,c)[~dog]}.play
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)
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(
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~bird = 3;
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{PlayBuf.ar(10,c)[~bird]}.play
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)
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// copy at least one other component to a third filter, a sort of left-over channel
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(
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Routine{
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FluidBufCompose.process(s, x, startChan:~dog, numChans: 1, destination: e);
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FluidBufCompose.process(s, x, startChan:~bird, numChans: 1, destStartChan: 1, destination: e, destGain:1);
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(0..9).removeAll([~dog,~bird]).do({|chan|FluidBufCompose.process(s,x, startChan:chan, numChans: 1, destStartChan: 2, destination: e, destGain:1)});
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e.query;
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}.play;
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)
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e.plot;
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//using this trained basis we can then see the activation... (wait for 5 seconds before it prints!)
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(
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{
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var source, blips;
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//read the source
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source = PlayBuf.ar(2, b);
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blips = FluidNMFMatch.kr(source.sum,e,3);
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}.plot(5);
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)
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// ...and use some threshold to 'find' objects...
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(
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{
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var source, blips;
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//read the source
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source = PlayBuf.ar(2, b);
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blips = Schmidt.kr(FluidNMFMatch.kr(source.sum,e,3),0.5,[10,1,1000]);
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}.plot(5);
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)
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// ...and use these to sonify them
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(
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{
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var source, blips, dogs, birds;
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//read the source
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source = PlayBuf.ar(2, b);
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blips = Schmidt.kr(FluidNMFMatch.kr(source.sum,e,3),0.5,[10,1,1000]);
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dogs = SinOsc.ar(100,0,Lag.kr(blips[0],0.05,0.15));
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birds = SinOsc.ar(1000,0,Lag.kr(blips[1],0.05,0.05));
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[dogs, birds] + source;
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}.play;
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)
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@ -0,0 +1,51 @@
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// Using an 88-components piano base to do polyphonic pitch tracking
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//load in the sound in and a pretrained basis
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(
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b = Buffer.read(s,File.realpath(FluidNMFMatch.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/Tremblay-SA-UprightPianoPedalWide.wav");
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c = Buffer.read(s,File.realpath(FluidNMFMatch.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/filters/piano-dicts.wav");
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)
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b.play
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c.query
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//use the pretrained bases to compute activations of each notes to drive the amplitude of a resynth
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(
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{
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var source, resynth;
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source = PlayBuf.ar(2, b,loop:1).sum;
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resynth = SinOsc.ar((21..108).midicps, 0, FluidNMFMatch.kr(source,c,88,10,4096).madd(0.002)).sum;
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[source, resynth]
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}.play
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)
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//now sample and hold the same stream to get notes identified, played and sent back via osc
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(
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{
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var source, resynth, chain, trig, acts;
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source = PlayBuf.ar(2,b,loop:1).sum;
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// built in attack detection, delayed until the stable part of the sound
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chain = FFT(LocalBuf(256), source);
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trig = TDelay.kr(Onsets.kr(chain, 0.5),0.1);
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// samples and holds activation values that are scaled and capped, in effect thresholding them
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acts = Latch.kr(FluidNMFMatch.kr(source,c,88,10,4096).linlin(15,20,0,0.1),trig);
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// resynths as in the previous example, with the values sent back to the language
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resynth = SinOsc.ar((21..108).midicps, 0, acts).sum;
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SendReply.kr(trig, '/activations', acts);
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[source, resynth]
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// [source, T2A.ar(trig)]
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// resynth
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}.play
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)
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// define a receiver for the activations
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(
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OSCdef(\listener, {|msg|
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var data = msg[3..];
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// removes the silent and spits out the indicies as midinote number
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data.collect({arg item, i; if (item > 0.01, {i + 21})}).reject({arg item; item.isNil}).postln;
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}, '/activations');
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)
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