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help pitch and buf pitch finished (plus typo in nmffilter)

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nix
Pierre Alexandre Tremblay 7 years ago
parent 117ccd55f8
commit 7f5bd09321
3 changed files with 81 additions and 19 deletions
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88
release-packaging/HelpSource/Classes/FluidBufPitch.schelp
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@ -7,7 +7,7 @@ RELATED:: Guides/FluCoMa, Guides/FluidDecomposition, Classes/SpecCentroid, Class
DESCRIPTION::
This class implements three popular pitch descriptors, computed as frequency and the confidence in its value. 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 process will return a multichannel buffer with two channels per input channel, one for pitch and one for the pitch tracking confidence. Each sample represents a value, which is every hopSize.
The process will return a multichannel buffer with two channels per input channel, one for pitch and one for the pitch tracking confidence. Each sample represents a value, which is every hopSize. Its sampling rate is sourceSR / hopSize.
CLASSMETHODS::
@ -33,14 +33,14 @@ ARGUMENT:: numChans
For multichannel srcBuf, how many channel should be processed.
ARGUMENT:: features
The destination buffer for the pitch descriptor.
The destination buffer for the pitch descriptors.
ARGUMENT:: algorithm
The algorithm to estimate the pitch. The options are:
The algorithm to estimate the pitch. The options are:
TABLE::
## 0 || Cepstrum: TODO.
## 1 || Harmonic Product Spectrum: TODO.
## 2 || YinFFT: TODO.
## 0 || Cepstrum: Returns a pitch estimate as the location of the second highest peak in the Cepstrum of the signal (after DC).
## 1 || Harmonic Product Spectrum: Implements the Harmonic Product Spectrum algorithm for pitch detection . See e.g. FOOTNOTE:: A. Lerch, "An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics." John Wiley & Sons, 2012.https://onlinelibrary.wiley.com/doi/book/10.1002/9781118393550 ::
## 2 || YinFFT: Implements the frequency domain version of the YIN algorithm, as described in FOOTNOTE::P. M. Brossier, "Automatic Annotation of Musical Audio for Interactive Applications.” QMUL, London, UK, 2007. :: See also https://essentia.upf.edu/documentation/reference/streaming_PitchYinFFT.html
::
ARGUMENT:: winSize
@ -61,12 +61,15 @@ RETURNS::
EXAMPLES::
code::
// create some buffers
// create a buffer with a short clicking sinusoidal burst (220Hz) starting at frame 8192 for 1024 frames
(
b = Buffer.read(s,File.realpath(FluidBufPitch.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/Tremblay-ASWINE-ScratchySynth-M.wav");
b = Buffer.sendCollection(s, (Array.fill(8192,{0}) ++ (Signal.sineFill(1203,[0,0,0,0,0,1],[0,0,0,0,0,0.5pi]).takeThese({|x,i|i>1023})) ++ Array.fill(8192,{0})));
c = Buffer.new(s);
)
// listen to the source and look at the buffer
b.play; b.plot;
// run the process with basic parameters
(
Routine{
@ -76,13 +79,16 @@ Routine{
}.play
)
// listen to the source and look at the buffer
b.play;
c.plot(minval:0, maxval:20000)
// look at the analysis
c.plot(minval:0, maxval:400)
// plot with a different range to appreciate the confidence:
c.plot(minval:0, maxval:1)
// interleaved [pitch,confidence] values in the buffer
c.getn(0,100,{|x|x.postln})
// The values are interleaved [pitch,confidence] in the buffer as they are on 2 channels: to get to the right frame, divide the SR of the input by the hopesize, then multiply by 2 because of the channel interleaving
// here we are querying from one frame before (the signal starts at 8192, which is frame 16 (8192/512), therefore starting the query at frame 15, which is index 30.
c.getn(30,10,{|x|x.postln})
// observe that the first frame is silent, as expected. The next frame's confidence is low-ish, because the window is half full (window of 1024, overlap of 512). Then a full window is analysed, with strong confidence. Then another half full window, then silence, as expected.
::
STRONG::A stereo buffer example.::
@ -112,4 +118,60 @@ Routine{
// look at the buffer: [pitch,confidence] for left then [pitch,confidence] for right
c.plot(minval:0, maxval:1500)
::
STRONG::A musical example.::
code::
// create some buffers
(
b = Buffer.read(s,File.realpath(FluidBufPitch.class.filenameSymbol).dirname.withTrailingSlash ++ "../AudioFiles/Tremblay-ASWINE-ScratchySynth-M.wav");
c = Buffer.new(s);
)
// run the process with basic parameters and retrieve the array in the langage side
(
Routine{
t = Main.elapsedTime;
FluidBufPitch.process(s, b, features: c,action:{c.loadToFloatArray(action: {|x| d = x.reshape((x.size()/2).asInt, 2)})});
(Main.elapsedTime - t).postln;
}.play
)
//look at the retrieved formatted array of [pitch,confidence] values
d.postln
//iterate and make an array of the indices which are fitting the conditions
(
e = Array.new;
d.do({
arg val, i;
if ((val[0] > 500) && (val[1] > 0.666)) {e = e.add(i)}; // if pitch is greater than 500Hz and confidence higher than 0.666, keep the index
});
)
e.postln;
//granulate only the frames that are in our buffer
// We need to convert our indices to frame start. Their position was (index * hopSize) - (winSize) in FluidBufPitch
f = e.collect({arg i; (i * 512) - 1024});
// define a basic grain synth
(
SynthDef(\grain,
{ arg out=0, buf =0 , ind = 0, pan = 0;
var env;
env = EnvGen.kr(Env.new([0,1,0],[512/s.sampleRate].dup,\sine), doneAction: Done.freeSelf);
Out.ar(out, Pan2.ar(PlayBuf.ar(1,buf,startPos:ind),pan));
}).add;
)
// start the sequence
(
a = Pxrand(f, inf).asStream;
Routine({
loop({
Synth(\grain, [\buf, b, \ind, a.next, \pan, (2.0.rand - 1)]);
(256/s.sampleRate).wait;
})
}).play;
)
::

3
release-packaging/HelpSource/Classes/FluidNMFFilter.schelp
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@ -205,7 +205,8 @@ f.free;
// more fun: processing the 3 rank independently
(
f = {var source, x,y,z, rev, dist, bases = c.bufnum;
f = {arg bases = c.bufnum;
var source, x,y,z, rev, dist;
source = In.ar(0,2);
#x,y,z = FluidNMFFilter.ar(source.sum, bases, 3, winSize:2048);
rev = FreeVerb.ar(x);

9
release-packaging/HelpSource/Classes/FluidPitch.schelp
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@ -17,14 +17,13 @@ ARGUMENT:: in
The audio to be processed.
ARGUMENT:: algorithm
The algorithm to estimate the pitch. The options are:
The algorithm to estimate the pitch. The options are:
TABLE::
## 0 || Cepstrum: TODO.
## 1 || Harmonic Product Spectrum: TODO.
## 2 || YinFFT: TODO.
## 0 || Cepstrum: Returns a pitch estimate as the location of the second highest peak in the Cepstrum of the signal (after DC).
## 1 || Harmonic Product Spectrum: Implements the Harmonic Product Spectrum algorithm for pitch detection . See e.g. FOOTNOTE:: A. Lerch, "An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics." John Wiley & Sons, 2012.https://onlinelibrary.wiley.com/doi/book/10.1002/9781118393550 ::
## 2 || YinFFT: Implements the frequency domain version of the YIN algorithm, as described in FOOTNOTE::P. M. Brossier, "Automatic Annotation of Musical Audio for Interactive Applications.” QMUL, London, UK, 2007. :: See also https://essentia.upf.edu/documentation/reference/streaming_PitchYinFFT.html
::
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

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