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77 lines
2.2 KiB
Plaintext
77 lines
2.2 KiB
Plaintext
TITLE:: FluidBufToKr
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summary:: Helper pseudo UGen for reading data out of a buffer to a Kr stream
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categories:: Libraries>FluidCorpusManipulation
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related:: Classes/FluidKrToBuf
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DESCRIPTION::
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Helper pseudo UGen for reading data out of a buffer to a Kr stream. It only reads one-channel buffers, converting them to a Kr stream.
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CLASSMETHODS::
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METHOD:: kr
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Initialize an instance of this pseudo UGen
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ARGUMENT:: buffer
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Either a link::Classes/Buffer:: object or an index opointing to a buffer that this pseudo UGen will read out of. Must be a one-channel buffer.
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ARGUMENT:: startFrame
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Offset of reading position in the buffer. The default is 0.
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ARGUMENT:: numFrames
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Number of frames to read from the buffer. Needs to be set, if buffer is not a code::Buffer:: object but a buffer index. If code::-1::, read whole buffer starting at code::startFrame::. The default is -1.
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returns:: a Kr stream that has the same number of channels as frames in the link::Classes/Buffer::.
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INSTANCEMETHODS::
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EXAMPLES::
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code::
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// fill a 1-channel buffer with 7 numbers
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~buf = Buffer.loadCollection(s,{exprand(100,4000)} ! 7);
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// in a synth, read those numbers out of the buffer and get them as a control stream
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(
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~synth = {
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arg buf;
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var freqs = FluidBufToKr.kr(buf,numFrames:7);
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var sig = SinOsc.ar(freqs.lag(0.03)) * 0.1;
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sig.poll;
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Splay.ar(sig);
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}.play(args:[\buf,~buf]);
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)
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// then you can change what's in the buffer and it will get read out by the synth
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~buf.setn(0,{exprand(100,4000)} ! 7);
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::
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Use with other FluCoMa objects:
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code::
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// create an neural network for classification
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~mlp = FluidMLPClassifier(s);
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// load a model that has been pre-trained to classify between a tone and noise, simple, i know, but...
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~mlp.read(FluidFilesPath("../Resources/bufToKrExample.json"));
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// can be used to demonstrate that...
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(
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{
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var input_buf = LocalBuf(7);
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var out_buf = LocalBuf(1);
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var sig = Select.ar(ToggleFF.kr(Dust.kr(1)),[SinOsc.ar(440),PinkNoise.ar]);
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var analysis = FluidSpectralShape.kr(sig);
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FluidKrToBuf.kr(analysis,input_buf);
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// the output prediction is written into a buffer
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~mlp.kr(Impulse.kr(30),input_buf,out_buf);
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// and FluidBufToKr can be used to read the prediction out into a control rate stream
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FluidBufToKr.kr(out_buf).poll;
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sig.dup * -30.dbamp
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}.play;
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)
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