57 lines
1.9 KiB
Text
57 lines
1.9 KiB
Text
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class:: SpecPcile
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summary:: Find a percentile of FFT magnitude spectrum
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categories:: UGens>FFT
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related:: Classes/SpecCentroid, Classes/SpecFlatness
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description::
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Given an link::Classes/FFT:: chain this calculates the cumulative distribution of the frequency spectrum, and outputs the frequency value which corresponds to the desired percentile.
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For example, to find the frequency at which 90% of the spectral energy lies below that frequency, you want the 90-percentile, which means the value of emphasis::fraction:: should be 0.9. The 90-percentile or 95-percentile is often used as a measure of strong::spectral roll-off::.
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The optional third argument strong::interpolate:: specifies whether interpolation should be used to try and make the percentile frequency estimate more accurate, at the cost of a little higher CPU usage. Set it to 1 to enable this.
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classmethods::
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method:: kr
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argument:: buffer
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an link::Classes/FFT:: chain.
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argument:: fraction
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argument:: interpolate
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examples::
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code::
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s.boot;
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b = Buffer.alloc(s,2048,1);
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// Simple demo with filtering white noise, and trying to infer the cutoff freq.
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// Move the mouse.
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(
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{
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var in, chain, realcutoff, estcutoff;
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realcutoff = MouseX.kr(0.00001,22050);
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in = LPF.ar(WhiteNoise.ar, realcutoff);
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chain = FFT(b, in);
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estcutoff = Lag.kr(SpecPcile.kr(chain, 0.9), 1);
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realcutoff.poll(Impulse.kr(1), "real cutoff");
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estcutoff.poll(Impulse.kr(1), "estimated cutoff");
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Out.ar(0, in);
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Out.kr(0, estcutoff * 22050.0.reciprocal);
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}.scope;
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)
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// Audio input - try different vowel/long-consonant sounds and see what comes out.
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// Specifically, change from "ssss" through to "aaaa" through to "wwww".
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(
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{
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var in, chain, perc;
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in = SoundIn.ar([0,1]).mean;
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chain = FFT(b, in);
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//Out.ar(0, in * 0.1);
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perc = SpecPcile.kr(chain, 0.5);
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Out.ar(1, LPF.ar(WhiteNoise.ar, perc)); //NB Outputting to right channel - handy on PowerBooks
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Out.kr(0, perc * 22050.0.reciprocal);
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}.scope;
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)
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::
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