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livetrax/libs/vamp-pyin/PYinVamp.cpp

609 lines
16 KiB
C++

/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*- vi:set ts=8 sts=4 sw=4: */
/*
pYIN - A fundamental frequency estimator for monophonic audio
Centre for Digital Music, Queen Mary, University of London.
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation; either version 2 of the
License, or (at your option) any later version. See the file
COPYING included with this distribution for more information.
*/
#include "PYinVamp.h"
#include "MonoNote.h"
#include "MonoPitch.h"
#include "vamp-sdk/FFT.h"
#include <vector>
#include <algorithm>
#include <cstdio>
#include <cmath>
#include <complex>
using std::string;
using std::vector;
using Vamp::RealTime;
PYinVamp::PYinVamp(float inputSampleRate) :
Plugin(inputSampleRate),
m_channels(0),
m_stepSize(256),
m_blockSize(2048),
m_fmin(40),
m_fmax(1600),
m_yin(2048, inputSampleRate, 0.0),
m_oF0Candidates(0),
m_oF0Probs(0),
m_oVoicedProb(0),
m_oCandidateSalience(0),
m_oSmoothedPitchTrack(0),
m_oNotes(0),
m_threshDistr(2.0f),
m_outputUnvoiced(0.0f),
m_preciseTime(0.0f),
m_lowAmp(0.1f),
m_onsetSensitivity(0.7f),
m_pruneThresh(0.1f),
m_pitchProb(0),
m_timestamp(0),
m_level(0)
{
}
PYinVamp::~PYinVamp()
{
}
string
PYinVamp::getIdentifier() const
{
return "pyin";
}
string
PYinVamp::getName() const
{
return "pYin";
}
string
PYinVamp::getDescription() const
{
return "Monophonic pitch and note tracking based on a probabilistic Yin extension.";
}
string
PYinVamp::getMaker() const
{
return "Matthias Mauch";
}
int
PYinVamp::getPluginVersion() const
{
// Increment this each time you release a version that behaves
// differently from the previous one
return 2;
}
string
PYinVamp::getCopyright() const
{
return "GPL";
}
PYinVamp::InputDomain
PYinVamp::getInputDomain() const
{
return TimeDomain;
}
size_t
PYinVamp::getPreferredBlockSize() const
{
return 2048;
}
size_t
PYinVamp::getPreferredStepSize() const
{
return 256;
}
size_t
PYinVamp::getMinChannelCount() const
{
return 1;
}
size_t
PYinVamp::getMaxChannelCount() const
{
return 1;
}
PYinVamp::ParameterList
PYinVamp::getParameterDescriptors() const
{
ParameterList list;
ParameterDescriptor d;
d.identifier = "threshdistr";
d.name = "Yin threshold distribution";
d.description = ".";
d.unit = "";
d.minValue = 0.0f;
d.maxValue = 7.0f;
d.defaultValue = 2.0f;
d.isQuantized = true;
d.quantizeStep = 1.0f;
d.valueNames.push_back("Uniform");
d.valueNames.push_back("Beta (mean 0.10)");
d.valueNames.push_back("Beta (mean 0.15)");
d.valueNames.push_back("Beta (mean 0.20)");
d.valueNames.push_back("Beta (mean 0.30)");
d.valueNames.push_back("Single Value 0.10");
d.valueNames.push_back("Single Value 0.15");
d.valueNames.push_back("Single Value 0.20");
list.push_back(d);
d.identifier = "outputunvoiced";
d.valueNames.clear();
d.name = "Output estimates classified as unvoiced?";
d.description = ".";
d.unit = "";
d.minValue = 0.0f;
d.maxValue = 2.0f;
d.defaultValue = 0.0f;
d.isQuantized = true;
d.quantizeStep = 1.0f;
d.valueNames.push_back("No");
d.valueNames.push_back("Yes");
d.valueNames.push_back("Yes, as negative frequencies");
list.push_back(d);
d.identifier = "precisetime";
d.valueNames.clear();
d.name = "Use non-standard precise YIN timing (slow).";
d.description = ".";
d.unit = "";
d.minValue = 0.0f;
d.maxValue = 1.0f;
d.defaultValue = 0.0f;
d.isQuantized = true;
d.quantizeStep = 1.0f;
list.push_back(d);
d.identifier = "lowampsuppression";
d.valueNames.clear();
d.name = "Suppress low amplitude pitch estimates.";
d.description = ".";
d.unit = "";
d.minValue = 0.0f;
d.maxValue = 1.0f;
d.defaultValue = 0.1f;
d.isQuantized = false;
list.push_back(d);
d.identifier = "onsetsensitivity";
d.valueNames.clear();
d.name = "Onset sensitivity";
d.description = "Adds additional note onsets when RMS increases.";
d.unit = "";
d.minValue = 0.0f;
d.maxValue = 1.0f;
d.defaultValue = 0.7f;
d.isQuantized = false;
list.push_back(d);
d.identifier = "prunethresh";
d.valueNames.clear();
d.name = "Duration pruning threshold.";
d.description = "Prune notes that are shorter than this value.";
d.unit = "";
d.minValue = 0.0f;
d.maxValue = 0.2f;
d.defaultValue = 0.1f;
d.isQuantized = false;
list.push_back(d);
return list;
}
float
PYinVamp::getParameter(string identifier) const
{
if (identifier == "threshdistr") {
return m_threshDistr;
}
if (identifier == "outputunvoiced") {
return m_outputUnvoiced;
}
if (identifier == "precisetime") {
return m_preciseTime;
}
if (identifier == "lowampsuppression") {
return m_lowAmp;
}
if (identifier == "onsetsensitivity") {
return m_onsetSensitivity;
}
if (identifier == "prunethresh") {
return m_pruneThresh;
}
return 0.f;
}
void
PYinVamp::setParameter(string identifier, float value)
{
if (identifier == "threshdistr")
{
m_threshDistr = value;
}
if (identifier == "outputunvoiced")
{
m_outputUnvoiced = value;
}
if (identifier == "precisetime")
{
m_preciseTime = value;
}
if (identifier == "lowampsuppression")
{
m_lowAmp = value;
}
if (identifier == "onsetsensitivity")
{
m_onsetSensitivity = value;
}
if (identifier == "prunethresh")
{
m_pruneThresh = value;
}
}
PYinVamp::ProgramList
PYinVamp::getPrograms() const
{
ProgramList list;
return list;
}
string
PYinVamp::getCurrentProgram() const
{
return ""; // no programs
}
void
PYinVamp::selectProgram(string name)
{
}
PYinVamp::OutputList
PYinVamp::getOutputDescriptors() const
{
OutputList outputs;
OutputDescriptor d;
int outputNumber = 0;
d.identifier = "f0candidates";
d.name = "F0 Candidates";
d.description = "Estimated fundamental frequency candidates.";
d.unit = "Hz";
d.hasFixedBinCount = false;
// d.binCount = 1;
d.hasKnownExtents = true;
d.minValue = m_fmin;
d.maxValue = 500;
d.isQuantized = false;
d.sampleType = OutputDescriptor::FixedSampleRate;
d.sampleRate = (m_inputSampleRate / m_stepSize);
d.hasDuration = false;
outputs.push_back(d);
m_oF0Candidates = outputNumber++;
d.identifier = "f0probs";
d.name = "Candidate Probabilities";
d.description = "Probabilities of estimated fundamental frequency candidates.";
d.unit = "";
d.hasFixedBinCount = false;
// d.binCount = 1;
d.hasKnownExtents = true;
d.minValue = 0;
d.maxValue = 1;
d.isQuantized = false;
d.sampleType = OutputDescriptor::FixedSampleRate;
d.sampleRate = (m_inputSampleRate / m_stepSize);
d.hasDuration = false;
outputs.push_back(d);
m_oF0Probs = outputNumber++;
d.identifier = "voicedprob";
d.name = "Voiced Probability";
d.description = "Probability that the signal is voiced according to Probabilistic Yin.";
d.unit = "";
d.hasFixedBinCount = true;
d.binCount = 1;
d.hasKnownExtents = true;
d.minValue = 0;
d.maxValue = 1;
d.isQuantized = false;
d.sampleType = OutputDescriptor::FixedSampleRate;
d.sampleRate = (m_inputSampleRate / m_stepSize);
d.hasDuration = false;
outputs.push_back(d);
m_oVoicedProb = outputNumber++;
d.identifier = "candidatesalience";
d.name = "Candidate Salience";
d.description = "Candidate Salience";
d.hasFixedBinCount = true;
d.binCount = m_blockSize / 2;
d.hasKnownExtents = true;
d.minValue = 0;
d.maxValue = 1;
d.isQuantized = false;
d.sampleType = OutputDescriptor::FixedSampleRate;
d.sampleRate = (m_inputSampleRate / m_stepSize);
d.hasDuration = false;
outputs.push_back(d);
m_oCandidateSalience = outputNumber++;
d.identifier = "smoothedpitchtrack";
d.name = "Smoothed Pitch Track";
d.description = ".";
d.unit = "Hz";
d.hasFixedBinCount = true;
d.binCount = 1;
d.hasKnownExtents = false;
// d.minValue = 0;
// d.maxValue = 1;
d.isQuantized = false;
d.sampleType = OutputDescriptor::FixedSampleRate;
d.sampleRate = (m_inputSampleRate / m_stepSize);
d.hasDuration = false;
outputs.push_back(d);
m_oSmoothedPitchTrack = outputNumber++;
d.identifier = "notes";
d.name = "Notes";
d.description = "Derived fixed-pitch note frequencies";
// d.unit = "MIDI unit";
d.unit = "Hz";
d.hasFixedBinCount = true;
d.binCount = 1;
d.hasKnownExtents = false;
d.isQuantized = false;
d.sampleType = OutputDescriptor::VariableSampleRate;
d.sampleRate = (m_inputSampleRate / m_stepSize);
d.hasDuration = true;
outputs.push_back(d);
m_oNotes = outputNumber++;
return outputs;
}
bool
PYinVamp::initialise(size_t channels, size_t stepSize, size_t blockSize)
{
if (channels < getMinChannelCount() ||
channels > getMaxChannelCount()) return false;
/*
std::cerr << "PYinVamp::initialise: channels = " << channels
<< ", stepSize = " << stepSize << ", blockSize = " << blockSize
<< std::endl;
*/
m_channels = channels;
m_stepSize = stepSize;
m_blockSize = blockSize;
reset();
return true;
}
void
PYinVamp::reset()
{
m_yin.setThresholdDistr(m_threshDistr);
m_yin.setFrameSize(m_blockSize);
m_yin.setFast(!m_preciseTime);
m_pitchProb.clear();
m_timestamp.clear();
m_level.clear();
/*
std::cerr << "PYinVamp::reset"
<< ", blockSize = " << m_blockSize
<< std::endl;
*/
}
PYinVamp::FeatureSet
PYinVamp::process(const float *const *inputBuffers, RealTime timestamp)
{
int offset = m_preciseTime == 1.0 ? m_blockSize/2 : m_blockSize/4;
timestamp = timestamp + Vamp::RealTime::frame2RealTime(offset, lrintf(m_inputSampleRate));
FeatureSet fs;
float rms = 0;
double *dInputBuffers = new double[m_blockSize];
for (size_t i = 0; i < m_blockSize; ++i) {
dInputBuffers[i] = inputBuffers[0][i];
rms += inputBuffers[0][i] * inputBuffers[0][i];
}
rms /= m_blockSize;
rms = sqrt(rms);
bool isLowAmplitude = (rms < m_lowAmp);
Yin::YinOutput yo = m_yin.processProbabilisticYin(dInputBuffers);
delete [] dInputBuffers;
m_level.push_back(yo.rms);
// First, get the things out of the way that we don't want to output
// immediately, but instead save for later.
vector<pair<double, double> > tempPitchProb;
for (size_t iCandidate = 0; iCandidate < yo.freqProb.size(); ++iCandidate)
{
double tempPitch = 12 * std::log(yo.freqProb[iCandidate].first/440)/std::log(2.) + 69;
if (!isLowAmplitude)
{
tempPitchProb.push_back(pair<double, double>
(tempPitch, yo.freqProb[iCandidate].second));
} else {
float factor = ((rms+0.01*m_lowAmp)/(1.01*m_lowAmp));
tempPitchProb.push_back(pair<double, double>
(tempPitch, yo.freqProb[iCandidate].second*factor));
}
}
m_pitchProb.push_back(tempPitchProb);
m_timestamp.push_back(timestamp);
// F0 CANDIDATES
Feature f;
f.hasTimestamp = true;
f.timestamp = timestamp;
for (size_t i = 0; i < yo.freqProb.size(); ++i)
{
f.values.push_back(yo.freqProb[i].first);
}
fs[m_oF0Candidates].push_back(f);
// VOICEDPROB
f.values.clear();
float voicedProb = 0;
for (size_t i = 0; i < yo.freqProb.size(); ++i)
{
f.values.push_back(yo.freqProb[i].second);
voicedProb += yo.freqProb[i].second;
}
fs[m_oF0Probs].push_back(f);
f.values.push_back(voicedProb);
fs[m_oVoicedProb].push_back(f);
// SALIENCE -- maybe this should eventually disappear
f.values.clear();
float salienceSum = 0;
for (size_t iBin = 0; iBin < yo.salience.size(); ++iBin)
{
f.values.push_back(yo.salience[iBin]);
salienceSum += yo.salience[iBin];
}
fs[m_oCandidateSalience].push_back(f);
return fs;
}
PYinVamp::FeatureSet
PYinVamp::getRemainingFeatures()
{
FeatureSet fs;
Feature f;
f.hasTimestamp = true;
f.hasDuration = false;
if (m_pitchProb.empty()) {
return fs;
}
// MONO-PITCH STUFF
MonoPitch mp;
vector<float> mpOut = mp.process(m_pitchProb);
for (size_t iFrame = 0; iFrame < mpOut.size(); ++iFrame)
{
if (mpOut[iFrame] < 0 && (m_outputUnvoiced==0)) continue;
f.timestamp = m_timestamp[iFrame];
f.values.clear();
if (m_outputUnvoiced == 1)
{
f.values.push_back(fabs(mpOut[iFrame]));
} else {
f.values.push_back(mpOut[iFrame]);
}
fs[m_oSmoothedPitchTrack].push_back(f);
}
// MONO-NOTE STUFF
// std::cerr << "Mono Note Stuff" << std::endl;
MonoNote mn;
std::vector<std::vector<std::pair<double, double> > > smoothedPitch;
for (size_t iFrame = 0; iFrame < mpOut.size(); ++iFrame) {
std::vector<std::pair<double, double> > temp;
if (mpOut[iFrame] > 0)
{
double tempPitch = 12 * std::log(mpOut[iFrame]/440)/std::log(2.) + 69;
temp.push_back(std::pair<double,double>(tempPitch, .9));
}
smoothedPitch.push_back(temp);
}
// vector<MonoNote::FrameOutput> mnOut = mn.process(m_pitchProb);
vector<MonoNote::FrameOutput> mnOut = mn.process(smoothedPitch);
// turning feature into a note feature
f.hasTimestamp = true;
f.hasDuration = true;
f.values.clear();
int onsetFrame = 0;
bool isVoiced = 0;
bool oldIsVoiced = 0;
size_t nFrame = m_pitchProb.size();
float minNoteFrames = (m_inputSampleRate*m_pruneThresh) / m_stepSize;
std::vector<float> notePitchTrack; // collects pitches for one note at a time
for (size_t iFrame = 0; iFrame < nFrame; ++iFrame)
{
isVoiced = mnOut[iFrame].noteState < 3
&& smoothedPitch[iFrame].size() > 0
&& (iFrame >= nFrame-2
|| ((m_level[iFrame]/m_level[iFrame+2]) > m_onsetSensitivity));
// std::cerr << m_level[iFrame]/m_level[iFrame-1] << " " << isVoiced << std::endl;
if (isVoiced && iFrame != nFrame-1)
{
if (oldIsVoiced == 0) // beginning of a note
{
onsetFrame = iFrame;
}
float pitch = smoothedPitch[iFrame][0].first;
notePitchTrack.push_back(pitch); // add to the note's pitch track
} else { // not currently voiced
if (oldIsVoiced == 1) // end of note
{
// std::cerr << notePitchTrack.size() << " " << minNoteFrames << std::endl;
if (notePitchTrack.size() >= minNoteFrames)
{
std::sort(notePitchTrack.begin(), notePitchTrack.end());
float medianPitch = notePitchTrack[notePitchTrack.size()/2];
float medianFreq = std::pow(2,(medianPitch - 69) / 12) * 440;
f.values.clear();
f.values.push_back(medianFreq);
f.timestamp = m_timestamp[onsetFrame];
f.duration = m_timestamp[iFrame] - m_timestamp[onsetFrame];
fs[m_oNotes].push_back(f);
}
notePitchTrack.clear();
}
}
oldIsVoiced = isVoiced;
}
return fs;
}