flucoma-sc/release-packaging/HelpSource/Classes/FluidBufSpectralShape.schelp

TITLE:: FluidBufSpectralShape
SUMMARY:: Seven Spectral Shape Descriptors on a Buffer
CATEGORIES:: Libraries>FluidDecomposition
RELATED:: Guides/FluCoMa, Guides/FluidDecomposition, Classes/SpecCentroid, Classes/SpecFlatness, Classes/SpecCentroid, Classes/SpecPcile


DESCRIPTION::
This class implements seven of the most popular spectral shape descriptors, computed on a linear scale for both amplitude and frequency. 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).::

The descriptors are:
LIST::
##the four first statistical moments (https://en.wikipedia.org/wiki/Moment_(mathematics) ), more commonly known as:
	LIST::
	## the spectral centroid (1) in spectral bins as units. This is the point that splits the spectrum in 2 halves of equal energy. It is the weighted average of the magnitude spectrum.
	## the spectral spread (2) in spectral bins. This is the standard deviation of the spectrum envelop, or the average of the distance to the centroid.
	## the normalised skewness (3) as ratio. This indicates how tilted is the spectral curve in relation to the middle of the spectral frame, i.e. half of the Nyquist frequency. If it is below the bin representing that frequency, i.e. the central bin of the magnitude spectrum, it is positive.
	## the normalised kurtosis (4) as ratio. This indicates how focused is the spectral curve. If it is peaky, it is high.
	::
	## the rolloff (5) in bin number. This indicates the bin under which 95% of the energy is included.
	## the flatness (6) in dB. This is the ratio of geometric mean of the magnitude, over the arithmetic mean of the magnitudes. It yields a very approximate measure on how noisy a signal is.
	## the crest (7) in dB. This is the ratio of the loudest magnitude over the RMS of the whole frame. A high number is an indication of a loud peak poking out from the overal spectral curve.

	The drawings in Peeters 2003 (http://recherche.ircam.fr/anasyn/peeters/ARTICLES/Peeters_2003_cuidadoaudiofeatures.pdf) are useful, as are the commented examples below. For the mathematically-inclined reader, the tutorials and code offered here (https://www.audiocontentanalysis.org/) are interesting to further the understanding.
::

	The process will return a multichannel buffer with the seven channels per input channel, each containing the 7 shapes. Each sample represents a value, which is every hopSize.

CLASSMETHODS::

METHOD:: process
	This is the method that calls for the spectral shape descriptors to be calculated on a given source buffer.

ARGUMENT:: server
	The server on which the buffers to be processed are allocated.

ARGUMENT:: source
	The index of the buffer to use as the source material to be described through the various descriptors. The different channels of multichannel buffers will be processing sequentially.

ARGUMENT:: startFrame
	Where in the srcBuf should the process start, in sample.

ARGUMENT:: numFrames
	How many frames should be processed.

ARGUMENT:: startChan
	For multichannel srcBuf, which channel should be processed first.

ARGUMENT:: numChans
	For multichannel srcBuf, how many channel should be processed.

ARGUMENT:: features
	The destination buffer for the 7 spectral features describing the spectral shape.

ARGUMENT:: winSize
	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

ARGUMENT:: hopSize
	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.

ARGUMENT:: fftSize
	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.

ARGUMENT:: action
	A Function to be evaluated once the offline process has finished and all Buffer's instance variables have been updated on the client side. The function will be passed [features] as an argument.

RETURNS::
	Nothing, as the destination buffer is declared in the function call.

EXAMPLES::

code::
// create some buffers
(
b = Buffer.read(s,File.realpath(FluidBufSpectralShape.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/Nicol-LoopE-M.wav");
c = Buffer.new(s);
)

// run the process with basic parameters
(
Routine{
	t = Main.elapsedTime;
	FluidBufSpectralShape.process(s, b, features: c);
	(Main.elapsedTime - t).postln;
}.play
)

// listen to the source and look at the buffer
b.play;
c.plot(minval:-5, maxval:250)
::

STRONG::A stereo buffer example.::
CODE::

// load two very different files
(
b = Buffer.read(s,File.realpath(FluidBufSpectralShape.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/Tremblay-SA-UprightPianoPedalWide.wav");
c = Buffer.read(s,File.realpath(FluidBufSpectralShape.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/Tremblay-AaS-AcousticStrums-M.wav");
)

// composite one on left one on right as test signals
FluidBufCompose.process(s, c, numFrames:b.numFrames, startFrame:555000,destStartChan:1, destination:b)
b.play

// create a buffer as destinations
c = Buffer.new(s);

//run the process on them
(
Routine{
    t = Main.elapsedTime;
    FluidBufSpectralShape.process(s, b, features: c);
    (Main.elapsedTime - t).postln;
}.play
)

// look at the buffer: 7shapes for left, then 7 shapes for right
c.plot(minval:-25, maxval:150)
::