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livetrax/libs/vamp-plugins/BeatTrack.cpp

585 lines
16 KiB
C++

/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*- vi:set ts=8 sts=4 sw=4: */
/*
QM Vamp Plugin Set
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 "BeatTrack.h"
#include <dsp/onsets/DetectionFunction.h>
#include <dsp/onsets/PeakPicking.h>
#include <dsp/tempotracking/TempoTrack.h>
#include <dsp/tempotracking/TempoTrackV2.h>
using std::string;
using std::vector;
using std::cerr;
using std::endl;
float BeatTracker::m_stepSecs = 0.01161; // 512 samples at 44100
#define METHOD_OLD 0
#define METHOD_NEW 1
class BeatTrackerData
{
public:
BeatTrackerData(const DFConfig &config) : dfConfig(config) {
df = new DetectionFunction(config);
}
~BeatTrackerData() {
delete df;
}
void reset() {
delete df;
df = new DetectionFunction(dfConfig);
dfOutput.clear();
origin = Vamp::RealTime::zeroTime;
}
DFConfig dfConfig;
DetectionFunction *df;
vector<double> dfOutput;
Vamp::RealTime origin;
};
BeatTracker::BeatTracker(float inputSampleRate) :
Vamp::Plugin(inputSampleRate),
m_d(0),
m_method(METHOD_NEW),
m_dfType(DF_COMPLEXSD),
m_alpha(0.9), // MEPD new exposed parameter for beat tracker, default value = 0.9 (as old version)
m_tightness(4.),
m_inputtempo(120.), // MEPD new exposed parameter for beat tracker, default value = 120. (as old version)
m_constraintempo(false), // MEPD new exposed parameter for beat tracker, default value = false (as old version)
// calling the beat tracker with these default parameters will give the same output as the previous existing version
m_whiten(false)
{
}
BeatTracker::~BeatTracker()
{
delete m_d;
}
string
BeatTracker::getIdentifier() const
{
return "qm-tempotracker";
}
string
BeatTracker::getName() const
{
return "Tempo and Beat Tracker";
}
string
BeatTracker::getDescription() const
{
return "Estimate beat locations and tempo";
}
string
BeatTracker::getMaker() const
{
return "Queen Mary, University of London";
}
int
BeatTracker::getPluginVersion() const
{
return 6;
}
string
BeatTracker::getCopyright() const
{
return "Plugin by Christian Landone and Matthew Davies. Copyright (c) 2006-2013 QMUL - All Rights Reserved";
}
BeatTracker::ParameterList
BeatTracker::getParameterDescriptors() const
{
ParameterList list;
ParameterDescriptor desc;
desc.identifier = "method";
desc.name = "Beat Tracking Method";
desc.description = "Basic method to use ";
desc.minValue = 0;
desc.maxValue = 1;
desc.defaultValue = METHOD_NEW;
desc.isQuantized = true;
desc.quantizeStep = 1;
desc.valueNames.push_back("Old");
desc.valueNames.push_back("New");
list.push_back(desc);
desc.identifier = "dftype";
desc.name = "Onset Detection Function Type";
desc.description = "Method used to calculate the onset detection function";
desc.minValue = 0;
desc.maxValue = 4;
desc.defaultValue = 3;
desc.valueNames.clear();
desc.valueNames.push_back("High-Frequency Content");
desc.valueNames.push_back("Spectral Difference");
desc.valueNames.push_back("Phase Deviation");
desc.valueNames.push_back("Complex Domain");
desc.valueNames.push_back("Broadband Energy Rise");
list.push_back(desc);
desc.identifier = "whiten";
desc.name = "Adaptive Whitening";
desc.description = "Normalize frequency bin magnitudes relative to recent peak levels";
desc.minValue = 0;
desc.maxValue = 1;
desc.defaultValue = 0;
desc.isQuantized = true;
desc.quantizeStep = 1;
desc.unit = "";
desc.valueNames.clear();
list.push_back(desc);
// MEPD new exposed parameter - used in the dynamic programming part of the beat tracker
//Alpha Parameter of Beat Tracker
desc.identifier = "alpha";
desc.name = "Alpha";
desc.description = "Inertia - Flexibility Trade Off";
desc.minValue = 0.1;
desc.maxValue = 0.99;
desc.defaultValue = 0.90;
desc.unit = "";
desc.isQuantized = false;
list.push_back(desc);
// We aren't exposing tightness as a parameter, it's fixed at 4
// MEPD new exposed parameter - used in the periodicity estimation
//User input tempo
desc.identifier = "inputtempo";
desc.name = "Tempo Hint";
desc.description = "User-defined tempo on which to centre the tempo preference function";
desc.minValue = 50;
desc.maxValue = 250;
desc.defaultValue = 120;
desc.unit = "BPM";
desc.isQuantized = true;
list.push_back(desc);
// MEPD new exposed parameter - used in periodicity estimation
desc.identifier = "constraintempo";
desc.name = "Constrain Tempo";
desc.description = "Constrain more tightly around the tempo hint, using a Gaussian weighting instead of Rayleigh";
desc.minValue = 0;
desc.maxValue = 1;
desc.defaultValue = 0;
desc.isQuantized = true;
desc.quantizeStep = 1;
desc.unit = "";
desc.valueNames.clear();
list.push_back(desc);
return list;
}
float
BeatTracker::getParameter(std::string name) const
{
if (name == "dftype") {
switch (m_dfType) {
case DF_HFC: return 0;
case DF_SPECDIFF: return 1;
case DF_PHASEDEV: return 2;
default: case DF_COMPLEXSD: return 3;
case DF_BROADBAND: return 4;
}
} else if (name == "method") {
return m_method;
} else if (name == "whiten") {
return m_whiten ? 1.0 : 0.0;
} else if (name == "alpha") {
return m_alpha;
} else if (name == "inputtempo") {
return m_inputtempo;
} else if (name == "constraintempo") {
return m_constraintempo ? 1.0 : 0.0;
}
return 0.0;
}
void
BeatTracker::setParameter(std::string name, float value)
{
if (name == "dftype") {
switch (lrintf(value)) {
case 0: m_dfType = DF_HFC; break;
case 1: m_dfType = DF_SPECDIFF; break;
case 2: m_dfType = DF_PHASEDEV; break;
default: case 3: m_dfType = DF_COMPLEXSD; break;
case 4: m_dfType = DF_BROADBAND; break;
}
} else if (name == "method") {
m_method = lrintf(value);
} else if (name == "whiten") {
m_whiten = (value > 0.5);
} else if (name == "alpha") {
m_alpha = value;
} else if (name == "inputtempo") {
m_inputtempo = value;
} else if (name == "constraintempo") {
m_constraintempo = (value > 0.5);
}
}
bool
BeatTracker::initialise(size_t channels, size_t stepSize, size_t blockSize)
{
if (m_d) {
delete m_d;
m_d = 0;
}
if (channels < getMinChannelCount() ||
channels > getMaxChannelCount()) {
std::cerr << "BeatTracker::initialise: Unsupported channel count: "
<< channels << std::endl;
return false;
}
if (stepSize != getPreferredStepSize()) {
std::cerr << "ERROR: BeatTracker::initialise: Unsupported step size for this sample rate: "
<< stepSize << " (wanted " << (getPreferredStepSize()) << ")" << std::endl;
return false;
}
if (blockSize != getPreferredBlockSize()) {
std::cerr << "WARNING: BeatTracker::initialise: Sub-optimal block size for this sample rate: "
<< blockSize << " (wanted " << getPreferredBlockSize() << ")" << std::endl;
// return false;
}
DFConfig dfConfig;
dfConfig.DFType = m_dfType;
dfConfig.stepSize = stepSize;
dfConfig.frameLength = blockSize;
dfConfig.dbRise = 3;
dfConfig.adaptiveWhitening = m_whiten;
dfConfig.whiteningRelaxCoeff = -1;
dfConfig.whiteningFloor = -1;
m_d = new BeatTrackerData(dfConfig);
return true;
}
void
BeatTracker::reset()
{
if (m_d) m_d->reset();
}
size_t
BeatTracker::getPreferredStepSize() const
{
size_t step = size_t(m_inputSampleRate * m_stepSecs + 0.0001);
// std::cerr << "BeatTracker::getPreferredStepSize: input sample rate is " << m_inputSampleRate << ", step size is " << step << std::endl;
return step;
}
size_t
BeatTracker::getPreferredBlockSize() const
{
size_t theoretical = getPreferredStepSize() * 2;
// I think this is not necessarily going to be a power of two, and
// the host might have a problem with that, but I'm not sure we
// can do much about it here
return theoretical;
}
BeatTracker::OutputList
BeatTracker::getOutputDescriptors() const
{
OutputList list;
OutputDescriptor beat;
beat.identifier = "beats";
beat.name = "Beats";
beat.description = "Estimated metrical beat locations";
beat.unit = "";
beat.hasFixedBinCount = true;
beat.binCount = 0;
beat.sampleType = OutputDescriptor::VariableSampleRate;
beat.sampleRate = 1.0 / m_stepSecs;
OutputDescriptor df;
df.identifier = "detection_fn";
df.name = "Onset Detection Function";
df.description = "Probability function of note onset likelihood";
df.unit = "";
df.hasFixedBinCount = true;
df.binCount = 1;
df.hasKnownExtents = false;
df.isQuantized = false;
df.sampleType = OutputDescriptor::OneSamplePerStep;
OutputDescriptor tempo;
tempo.identifier = "tempo";
tempo.name = "Tempo";
tempo.description = "Locked tempo estimates";
tempo.unit = "bpm";
tempo.hasFixedBinCount = true;
tempo.binCount = 1;
tempo.hasKnownExtents = false;
tempo.isQuantized = false;
tempo.sampleType = OutputDescriptor::VariableSampleRate;
tempo.sampleRate = 1.0 / m_stepSecs;
list.push_back(beat);
list.push_back(df);
list.push_back(tempo);
return list;
}
BeatTracker::FeatureSet
BeatTracker::process(const float *const *inputBuffers,
Vamp::RealTime timestamp)
{
if (!m_d) {
cerr << "ERROR: BeatTracker::process: "
<< "BeatTracker has not been initialised"
<< endl;
return FeatureSet();
}
size_t len = m_d->dfConfig.frameLength / 2 + 1;
double *reals = new double[len];
double *imags = new double[len];
// We only support a single input channel
for (size_t i = 0; i < len; ++i) {
reals[i] = inputBuffers[0][i*2];
imags[i] = inputBuffers[0][i*2+1];
}
double output = m_d->df->processFrequencyDomain(reals, imags);
delete[] reals;
delete[] imags;
if (m_d->dfOutput.empty()) m_d->origin = timestamp;
m_d->dfOutput.push_back(output);
FeatureSet returnFeatures;
Feature feature;
feature.hasTimestamp = false;
feature.values.push_back(output);
returnFeatures[1].push_back(feature); // detection function is output 1
return returnFeatures;
}
BeatTracker::FeatureSet
BeatTracker::getRemainingFeatures()
{
if (!m_d) {
cerr << "ERROR: BeatTracker::getRemainingFeatures: "
<< "BeatTracker has not been initialised"
<< endl;
return FeatureSet();
}
if (m_method == METHOD_OLD) return beatTrackOld();
else return beatTrackNew();
}
BeatTracker::FeatureSet
BeatTracker::beatTrackOld()
{
double aCoeffs[] = { 1.0000, -0.5949, 0.2348 };
double bCoeffs[] = { 0.1600, 0.3200, 0.1600 };
TTParams ttParams;
ttParams.winLength = 512;
ttParams.lagLength = 128;
ttParams.LPOrd = 2;
ttParams.LPACoeffs = aCoeffs;
ttParams.LPBCoeffs = bCoeffs;
ttParams.alpha = 9;
ttParams.WinT.post = 8;
ttParams.WinT.pre = 7;
TempoTrack tempoTracker(ttParams);
vector<double> tempi;
vector<int> beats = tempoTracker.process(m_d->dfOutput, &tempi);
FeatureSet returnFeatures;
char label[100];
for (size_t i = 0; i < beats.size(); ++i) {
size_t frame = beats[i] * m_d->dfConfig.stepSize;
Feature feature;
feature.hasTimestamp = true;
feature.timestamp = m_d->origin + Vamp::RealTime::frame2RealTime
(frame, lrintf(m_inputSampleRate));
float bpm = 0.0;
int frameIncrement = 0;
if (i < beats.size() - 1) {
frameIncrement = (beats[i+1] - beats[i]) * m_d->dfConfig.stepSize;
// one beat is frameIncrement frames, so there are
// samplerate/frameIncrement bps, so
// 60*samplerate/frameIncrement bpm
if (frameIncrement > 0) {
bpm = (60.0 * m_inputSampleRate) / frameIncrement;
bpm = int(bpm * 100.0 + 0.5) / 100.0;
sprintf(label, "%.2f bpm", bpm);
feature.label = label;
}
}
returnFeatures[0].push_back(feature); // beats are output 0
}
double prevTempo = 0.0;
for (size_t i = 0; i < tempi.size(); ++i) {
size_t frame = i * m_d->dfConfig.stepSize * ttParams.lagLength;
// std::cerr << "unit " << i << ", step size " << m_d->dfConfig.stepSize << ", hop " << ttParams.lagLength << ", frame = " << frame << std::endl;
if (tempi[i] > 1 && int(tempi[i] * 100) != int(prevTempo * 100)) {
Feature feature;
feature.hasTimestamp = true;
feature.timestamp = m_d->origin + Vamp::RealTime::frame2RealTime
(frame, lrintf(m_inputSampleRate));
feature.values.push_back(tempi[i]);
sprintf(label, "%.2f bpm", tempi[i]);
feature.label = label;
returnFeatures[2].push_back(feature); // tempo is output 2
prevTempo = tempi[i];
}
}
return returnFeatures;
}
BeatTracker::FeatureSet
BeatTracker::beatTrackNew()
{
vector<double> df;
vector<double> beatPeriod;
vector<double> tempi;
size_t nonZeroCount = m_d->dfOutput.size();
while (nonZeroCount > 0) {
if (m_d->dfOutput[nonZeroCount-1] > 0.0) {
break;
}
--nonZeroCount;
}
// std::cerr << "Note: nonZeroCount was " << m_d->dfOutput.size() << ", is now " << nonZeroCount << std::endl;
for (size_t i = 2; i < nonZeroCount; ++i) { // discard first two elts
df.push_back(m_d->dfOutput[i]);
beatPeriod.push_back(0.0);
}
if (df.empty()) return FeatureSet();
TempoTrackV2 tt(m_inputSampleRate, m_d->dfConfig.stepSize);
// MEPD - note this function is now passed 2 new parameters, m_inputtempo and m_constraintempo
tt.calculateBeatPeriod(df, beatPeriod, tempi, m_inputtempo, m_constraintempo);
vector<double> beats;
// MEPD - note this function is now passed 2 new parameters, m_alpha and m_tightness
tt.calculateBeats(df, beatPeriod, beats, m_alpha, m_tightness);
FeatureSet returnFeatures;
char label[100];
for (size_t i = 0; i < beats.size(); ++i) {
size_t frame = beats[i] * m_d->dfConfig.stepSize;
Feature feature;
feature.hasTimestamp = true;
feature.timestamp = m_d->origin + Vamp::RealTime::frame2RealTime
(frame, lrintf(m_inputSampleRate));
float bpm = 0.0;
int frameIncrement = 0;
if (i+1 < beats.size()) {
frameIncrement = (beats[i+1] - beats[i]) * m_d->dfConfig.stepSize;
// one beat is frameIncrement frames, so there are
// samplerate/frameIncrement bps, so
// 60*samplerate/frameIncrement bpm
if (frameIncrement > 0) {
bpm = (60.0 * m_inputSampleRate) / frameIncrement;
bpm = int(bpm * 100.0 + 0.5) / 100.0;
sprintf(label, "%.2f bpm", bpm);
feature.label = label;
}
}
returnFeatures[0].push_back(feature); // beats are output 0
}
double prevTempo = 0.0;
for (size_t i = 0; i < tempi.size(); ++i) {
size_t frame = i * m_d->dfConfig.stepSize;
if (tempi[i] > 1 && int(tempi[i] * 100) != int(prevTempo * 100)) {
Feature feature;
feature.hasTimestamp = true;
feature.timestamp = m_d->origin + Vamp::RealTime::frame2RealTime
(frame, lrintf(m_inputSampleRate));
feature.values.push_back(tempi[i]);
sprintf(label, "%.2f bpm", tempi[i]);
feature.label = label;
returnFeatures[2].push_back(feature); // tempo is output 2
prevTempo = tempi[i];
}
}
return returnFeatures;
}