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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.
//set some buffers
(
b = Buffer.read(s,File.realpath(FluidNMFMatch.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/Tremblay-BaB-SoundscapeGolcarWithDog.wav");
c = Buffer.new(s);
x = Buffer.new(s);
e = Buffer.new(s);
)
// train where all objects are present
(
Routine {
FluidBufNMF.process(s,b,130000,150000,0,1, c, x, components:10);
c.query;
}.play;
)
// wait for the query to print
// 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
(
~dog =1;
{PlayBuf.ar(10,c)[~dog]}.play
)
(
~bird = 3;
{PlayBuf.ar(10,c)[~bird]}.play
)
// copy at least one other component to a third filter, a sort of left-over channel
(
Routine{
FluidBufCompose.process(s, x, startChan:~dog, numChans: 1, destination: e);
FluidBufCompose.process(s, x, startChan:~bird, numChans: 1, destStartChan: 1, destination: e, destGain:1);
(0..9).removeAll([~dog,~bird]).do({|chan|FluidBufCompose.process(s,x, startChan:chan, numChans: 1, destStartChan: 2, destination: e, destGain:1)});
e.query;
}.play;
)
e.plot;
//using this trained basis we can then see the activation... (wait for 5 seconds before it prints!)
(
{
var source, blips;
//read the source
source = PlayBuf.ar(2, b);
blips = FluidNMFMatch.kr(source.sum,e,3);
}.plot(5);
)
// ...and use some threshold to 'find' objects...
(
{
var source, blips;
//read the source
source = PlayBuf.ar(2, b);
blips = Schmidt.kr(FluidNMFMatch.kr(source.sum,e,3),0.5,[10,1,1000]);
}.plot(5);
)
// ...and use these to sonify them
(
{
var source, blips, dogs, birds;
//read the source
source = PlayBuf.ar(2, b);
blips = Schmidt.kr(FluidNMFMatch.kr(source.sum,e,3),0.5,[10,1,1000]);
dogs = SinOsc.ar(100,0,Lag.kr(blips[0],0.05,0.15));
birds = SinOsc.ar(1000,0,Lag.kr(blips[1],0.05,0.05));
[dogs, birds] + source;
}.play;
)