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TITLE:: FluidSines
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SUMMARY:: Sinusoidal Modelling and Resynthesis
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CATEGORIES:: Libraries>FluidDecomposition
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RELATED:: Guides/FluCoMa, Guides/FluidDecomposition
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DESCRIPTION::
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This class applies a Sinusoidal Modelling process on its audio input. It implements a mix and match algorithms taken from classic papers. It is part of the Fluid Decomposition Toolkit of the FluCoMa project. footnote:: This was made possible thanks to the FluCoMa project ( http://www.flucoma.org/ ) funded by the European Research Council ( https://erc.europa.eu/ ) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 725899).::
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The algorithm will take an audio in, and will divide it in two parts: LIST::
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## a reconstruction of what it detects as sinusoidal;
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## a residual derived from the previous signal to allow null-summing::
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The whole process is based on the assumption that signal is made of pitched steady components that have a long-enough duration and are periodic enough to be perceived as such, that can be tracked, resynthesised and removed from the original, leaving behind what is considered as non-pitched, noisy, and/or transient. It first tracks the peaks, then checks if they are the continuation of a peak in previous spectral frames, by assigning them a track. More information on this model, and on how it links to musicianly thinking, are availabe in LINK::Guides/FluCoMa:: overview file.
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CLASSMETHODS::
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METHOD:: ar
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The audio rate version of the object.
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ARGUMENT:: in
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The input to be processed
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ARGUMENT:: bandwidth
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The width in bins of the fragment of the fft window that is considered a normal deviation for a potential continuous sinusoidal track. It has an effect on CPU cost: the widest is more accurate but more computationally expensive.
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ARGUMENT:: thresh
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The normalised threshold, between 0 an 1, to consider a peak as a sinusoidal component from the normalized cross-correlation.
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ARGUMENT:: minTrackLen
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The minimum duration, in spectral frames, for a sinusoidal track to be accepted as a partial. It allows to remove space-monkeys, but is more CPU intensive and might reject quick pitch material.
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ARGUMENT:: magWeight
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The weight of the magnitude proximity of a peak when trying to associate it to an existing track (relative to freqWeight - suggested between 0 to 1)
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ARGUMENT:: freqWeight
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The weight of the frequency proximity of a peak when trying to associate it to an existing track (relative to magWeight - suggested between 0 to 1)
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ARGUMENT:: winSize
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The window size. As sinusoidal estimation relies on spectral frames, we need to decide what precision we give it spectrally and temporally, in line with Gabor Uncertainty principles. http://www.subsurfwiki.org/wiki/Gabor_uncertainty
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ARGUMENT:: hopSize
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The window hope size. As sinusoidal estimation relies on spectral frames, we need to move the window forward. It can be any size but low overlap will create audible artefacts.
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ARGUMENT:: fftSize
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The inner FFT/IFFT size. It should be at least 4 samples long, at least the size of the window, and a power of 2. Making it larger allows an oversampling of the spectral precision.
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RETURNS::
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An array of two audio streams: [0] is the harmonic part extracted, [1] is the rest. The latency between the input and the output is (( hopSize * minTrackLen) + windowSize) samples.
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EXAMPLES::
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CODE::
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// load some audio to play
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b = Buffer.read(s,File.realpath(FluidSines.class.filenameSymbol).dirname.replace("Classes","AudioFiles/Tremblay-AaS-SynthTwoVoices-M.wav"));
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// run with basic parameters - left is sinusoidal model, right is residual
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{FluidSines.ar(PlayBuf.ar(1,b.bufnum,loop:1))}.play
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// interactive parameters with a narrower bandwidth
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{FluidSines.ar(PlayBuf.ar(1,b.bufnum,loop:1),30,MouseX.kr(),5)}.play
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// null test (the process add a latency of (( hopSize * minTrackLen) + windowSize) samples
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{var sig = PlayBuf.ar(1,b.bufnum,loop:1); [FluidSines.ar(sig).sum - DelayN.ar(sig, 1, ((( 512 * 15) + 2048)/ s.sampleRate))]}.play
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:: |