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livetrax/libs/vamp-plugins/SimilarityPlugin.cpp

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/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*- vi:set ts=8 sts=4 sw=4: */
/*
* SimilarityPlugin.cpp
*
* Copyright 2009 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 <iostream>
#include <cstdio>
#include "SimilarityPlugin.h"
#include "base/Pitch.h"
#include "dsp/mfcc/MFCC.h"
#include "dsp/chromagram/Chromagram.h"
#include "dsp/rateconversion/Decimator.h"
#include "dsp/rhythm/BeatSpectrum.h"
#include "maths/KLDivergence.h"
#include "maths/CosineDistance.h"
#include "maths/MathUtilities.h"
using std::string;
using std::vector;
using std::cerr;
using std::endl;
using std::ostringstream;
const float
SimilarityPlugin::m_noRhythm = 0.009;
const float
SimilarityPlugin::m_allRhythm = 0.991;
SimilarityPlugin::SimilarityPlugin(float inputSampleRate) :
Plugin(inputSampleRate),
m_type(TypeMFCC),
m_mfcc(0),
m_rhythmfcc(0),
m_chromagram(0),
m_decimator(0),
m_featureColumnSize(20),
m_rhythmWeighting(0.5f),
m_rhythmClipDuration(4.f), // seconds
m_rhythmClipOrigin(40.f), // seconds
m_rhythmClipFrameSize(0),
m_rhythmClipFrames(0),
m_rhythmColumnSize(20),
m_blockSize(0),
m_channels(0),
m_processRate(0),
m_frameNo(0),
m_done(false)
{
int rate = lrintf(m_inputSampleRate);
int internalRate = 22050;
int decimationFactor = rate / internalRate;
if (decimationFactor < 1) decimationFactor = 1;
// must be a power of two
while (decimationFactor & (decimationFactor - 1)) ++decimationFactor;
m_processRate = rate / decimationFactor; // may be 22050, 24000 etc
}
SimilarityPlugin::~SimilarityPlugin()
{
delete m_mfcc;
delete m_rhythmfcc;
delete m_chromagram;
delete m_decimator;
}
string
SimilarityPlugin::getIdentifier() const
{
return "qm-similarity";
}
string
SimilarityPlugin::getName() const
{
return "Similarity";
}
string
SimilarityPlugin::getDescription() const
{
return "Return a distance matrix for similarity between the input audio channels";
}
string
SimilarityPlugin::getMaker() const
{
return "Queen Mary, University of London";
}
int
SimilarityPlugin::getPluginVersion() const
{
return 1;
}
string
SimilarityPlugin::getCopyright() const
{
return "Plugin by Mark Levy, Kurt Jacobson and Chris Cannam. Copyright (c) 2009 QMUL - All Rights Reserved";
}
size_t
SimilarityPlugin::getMinChannelCount() const
{
return 1;
}
size_t
SimilarityPlugin::getMaxChannelCount() const
{
return 1024;
}
int
SimilarityPlugin::getDecimationFactor() const
{
int rate = lrintf(m_inputSampleRate);
return rate / m_processRate;
}
size_t
SimilarityPlugin::getPreferredStepSize() const
{
if (m_blockSize == 0) calculateBlockSize();
// there is also an assumption to this effect in process()
// (referring to m_fftSize/2 instead of a literal post-decimation
// step size):
return m_blockSize/2;
}
size_t
SimilarityPlugin::getPreferredBlockSize() const
{
if (m_blockSize == 0) calculateBlockSize();
return m_blockSize;
}
void
SimilarityPlugin::calculateBlockSize() const
{
if (m_blockSize != 0) return;
int decimationFactor = getDecimationFactor();
m_blockSize = 2048 * decimationFactor;
}
SimilarityPlugin::ParameterList SimilarityPlugin::getParameterDescriptors() const
{
ParameterList list;
ParameterDescriptor desc;
desc.identifier = "featureType";
desc.name = "Feature Type";
desc.description = "Audio feature used for similarity measure. Timbral: use the first 20 MFCCs (19 plus C0). Chromatic: use 12 bin-per-octave chroma. Rhythmic: compare beat spectra of short regions.";
desc.unit = "";
desc.minValue = 0;
desc.maxValue = 4;
desc.defaultValue = 1;
desc.isQuantized = true;
desc.quantizeStep = 1;
desc.valueNames.push_back("Timbre");
desc.valueNames.push_back("Timbre and Rhythm");
desc.valueNames.push_back("Chroma");
desc.valueNames.push_back("Chroma and Rhythm");
desc.valueNames.push_back("Rhythm only");
list.push_back(desc);
/*
desc.identifier = "rhythmWeighting";
desc.name = "Influence of Rhythm";
desc.description = "Proportion of similarity measure made up from rhythmic similarity component, from 0 (entirely timbral or chromatic) to 100 (entirely rhythmic).";
desc.unit = "%";
desc.minValue = 0;
desc.maxValue = 100;
desc.defaultValue = 0;
desc.isQuantized = false;
desc.valueNames.clear();
list.push_back(desc);
*/
return list;
}
float
SimilarityPlugin::getParameter(std::string param) const
{
if (param == "featureType") {
if (m_rhythmWeighting > m_allRhythm) {
return 4;
}
switch (m_type) {
case TypeMFCC:
if (m_rhythmWeighting < m_noRhythm) return 0;
else return 1;
break;
case TypeChroma:
if (m_rhythmWeighting < m_noRhythm) return 2;
else return 3;
break;
}
return 1;
// } else if (param == "rhythmWeighting") {
// return nearbyint(m_rhythmWeighting * 100.0);
}
std::cerr << "WARNING: SimilarityPlugin::getParameter: unknown parameter \""
<< param << "\"" << std::endl;
return 0.0;
}
void
SimilarityPlugin::setParameter(std::string param, float value)
{
if (param == "featureType") {
int v = int(value + 0.1);
Type newType = m_type;
switch (v) {
case 0: newType = TypeMFCC; m_rhythmWeighting = 0.0f; break;
case 1: newType = TypeMFCC; m_rhythmWeighting = 0.5f; break;
case 2: newType = TypeChroma; m_rhythmWeighting = 0.0f; break;
case 3: newType = TypeChroma; m_rhythmWeighting = 0.5f; break;
case 4: newType = TypeMFCC; m_rhythmWeighting = 1.f; break;
}
if (newType != m_type) m_blockSize = 0;
m_type = newType;
return;
// } else if (param == "rhythmWeighting") {
// m_rhythmWeighting = value / 100;
// return;
}
std::cerr << "WARNING: SimilarityPlugin::setParameter: unknown parameter \""
<< param << "\"" << std::endl;
}
SimilarityPlugin::OutputList
SimilarityPlugin::getOutputDescriptors() const
{
OutputList list;
OutputDescriptor similarity;
similarity.identifier = "distancematrix";
similarity.name = "Distance Matrix";
similarity.description = "Distance matrix for similarity metric. Smaller = more similar. Should be symmetrical.";
similarity.unit = "";
similarity.hasFixedBinCount = true;
similarity.binCount = m_channels;
similarity.hasKnownExtents = false;
similarity.isQuantized = false;
similarity.sampleType = OutputDescriptor::FixedSampleRate;
similarity.sampleRate = 1;
m_distanceMatrixOutput = list.size();
list.push_back(similarity);
OutputDescriptor simvec;
simvec.identifier = "distancevector";
simvec.name = "Distance from First Channel";
simvec.description = "Distance vector for similarity of each channel to the first channel. Smaller = more similar.";
simvec.unit = "";
simvec.hasFixedBinCount = true;
simvec.binCount = m_channels;
simvec.hasKnownExtents = false;
simvec.isQuantized = false;
simvec.sampleType = OutputDescriptor::FixedSampleRate;
simvec.sampleRate = 1;
m_distanceVectorOutput = list.size();
list.push_back(simvec);
OutputDescriptor sortvec;
sortvec.identifier = "sorteddistancevector";
sortvec.name = "Ordered Distances from First Channel";
sortvec.description = "Vector of the order of other channels in similarity to the first, followed by distance vector for similarity of each to the first. Smaller = more similar.";
sortvec.unit = "";
sortvec.hasFixedBinCount = true;
sortvec.binCount = m_channels;
sortvec.hasKnownExtents = false;
sortvec.isQuantized = false;
sortvec.sampleType = OutputDescriptor::FixedSampleRate;
sortvec.sampleRate = 1;
m_sortedVectorOutput = list.size();
list.push_back(sortvec);
OutputDescriptor means;
means.identifier = "means";
means.name = "Feature Means";
means.description = "Means of the feature bins. Feature time (sec) corresponds to input channel. Number of bins depends on selected feature type.";
means.unit = "";
means.hasFixedBinCount = true;
means.binCount = m_featureColumnSize;
means.hasKnownExtents = false;
means.isQuantized = false;
means.sampleType = OutputDescriptor::FixedSampleRate;
means.sampleRate = 1;
m_meansOutput = list.size();
list.push_back(means);
OutputDescriptor variances;
variances.identifier = "variances";
variances.name = "Feature Variances";
variances.description = "Variances of the feature bins. Feature time (sec) corresponds to input channel. Number of bins depends on selected feature type.";
variances.unit = "";
variances.hasFixedBinCount = true;
variances.binCount = m_featureColumnSize;
variances.hasKnownExtents = false;
variances.isQuantized = false;
variances.sampleType = OutputDescriptor::FixedSampleRate;
variances.sampleRate = 1;
m_variancesOutput = list.size();
list.push_back(variances);
OutputDescriptor beatspectrum;
beatspectrum.identifier = "beatspectrum";
beatspectrum.name = "Beat Spectra";
beatspectrum.description = "Rhythmic self-similarity vectors (beat spectra) for the input channels. Feature time (sec) corresponds to input channel. Not returned if rhythm weighting is zero.";
beatspectrum.unit = "";
if (m_rhythmClipFrames > 0) {
beatspectrum.hasFixedBinCount = true;
beatspectrum.binCount = m_rhythmClipFrames / 2;
} else {
beatspectrum.hasFixedBinCount = false;
}
beatspectrum.hasKnownExtents = false;
beatspectrum.isQuantized = false;
beatspectrum.sampleType = OutputDescriptor::FixedSampleRate;
beatspectrum.sampleRate = 1;
m_beatSpectraOutput = list.size();
list.push_back(beatspectrum);
return list;
}
bool
SimilarityPlugin::initialise(size_t channels, size_t stepSize, size_t blockSize)
{
if (channels < getMinChannelCount()) return false;
// Using more than getMaxChannelCount is not actually a problem
// for us. Using "incorrect" step and block sizes would be fine
// for timbral or chroma similarity, but will break rhythmic
// similarity, so we'd better enforce these.
if (stepSize != getPreferredStepSize()) {
std::cerr << "SimilarityPlugin::initialise: supplied step size "
<< stepSize << " differs from required step size "
<< getPreferredStepSize() << std::endl;
return false;
}
if (blockSize != getPreferredBlockSize()) {
std::cerr << "SimilarityPlugin::initialise: supplied block size "
<< blockSize << " differs from required block size "
<< getPreferredBlockSize() << std::endl;
return false;
}
m_blockSize = blockSize;
m_channels = channels;
m_lastNonEmptyFrame = std::vector<int>(m_channels);
for (int i = 0; i < m_channels; ++i) m_lastNonEmptyFrame[i] = -1;
m_emptyFrameCount = std::vector<int>(m_channels);
for (int i = 0; i < m_channels; ++i) m_emptyFrameCount[i] = 0;
m_frameNo = 0;
int decimationFactor = getDecimationFactor();
if (decimationFactor > 1) {
m_decimator = new Decimator(m_blockSize, decimationFactor);
}
if (m_type == TypeMFCC) {
m_featureColumnSize = 20;
MFCCConfig config(m_processRate);
config.fftsize = 2048;
config.nceps = m_featureColumnSize - 1;
config.want_c0 = true;
config.logpower = 1;
m_mfcc = new MFCC(config);
m_fftSize = m_mfcc->getfftlength();
m_rhythmClipFrameSize = m_fftSize / 4;
// std::cerr << "MFCC FS = " << config.FS << ", FFT size = " << m_fftSize<< std::endl;
} else if (m_type == TypeChroma) {
m_featureColumnSize = 12;
// For simplicity, aim to have the chroma fft size equal to
// 2048, the same as the mfcc fft size (so the input block
// size does not depend on the feature type and we can use the
// same processing parameters for rhythm etc). This is also
// why getPreferredBlockSize can confidently return 2048 * the
// decimation factor.
// The fft size for a chromagram is the filterbank Q value
// times the sample rate, divided by the minimum frequency,
// rounded up to the nearest power of two.
double q = 1.0 / (pow(2.0, (1.0 / 12.0)) - 1.0);
double fmin = (q * m_processRate) / 2048.0;
// Round fmin up to the nearest MIDI pitch multiple of 12.
// So long as fmin is greater than 12 to start with, this
// should not change the resulting fft size.
int pmin = Pitch::getPitchForFrequency(float(fmin));
pmin = ((pmin / 12) + 1) * 12;
fmin = Pitch::getFrequencyForPitch(pmin);
float fmax = Pitch::getFrequencyForPitch(pmin + 36);
ChromaConfig config;
config.FS = m_processRate;
config.min = fmin;
config.max = fmax;
config.BPO = 12;
config.CQThresh = 0.0054;
// We don't normalise the chromagram's columns individually;
// we normalise the mean at the end instead
config.normalise = MathUtilities::NormaliseNone;
m_chromagram = new Chromagram(config);
m_fftSize = m_chromagram->getFrameSize();
if (m_fftSize != 2048) {
std::cerr << "WARNING: SimilarityPlugin::initialise: Internal processing FFT size " << m_fftSize << " != expected size 2048 in chroma mode" << std::endl;
}
// std::cerr << "fftsize = " << m_fftSize << std::endl;
m_rhythmClipFrameSize = m_fftSize / 4;
// std::cerr << "m_rhythmClipFrameSize = " << m_rhythmClipFrameSize << std::endl;
// std::cerr << "min = "<< config.min << ", max = " << config.max << std::endl;
} else {
std::cerr << "SimilarityPlugin::initialise: internal error: unknown type " << m_type << std::endl;
return false;
}
if (needRhythm()) {
m_rhythmClipFrames =
int(ceil((m_rhythmClipDuration * m_processRate)
/ m_rhythmClipFrameSize));
// std::cerr << "SimilarityPlugin::initialise: rhythm clip requires "
// << m_rhythmClipFrames << " frames of size "
// << m_rhythmClipFrameSize << " at process rate "
// << m_processRate << " ( = "
// << (float(m_rhythmClipFrames * m_rhythmClipFrameSize) / m_processRate) << " sec )"
// << std::endl;
MFCCConfig config(m_processRate);
config.fftsize = m_rhythmClipFrameSize;
config.nceps = m_rhythmColumnSize - 1;
config.want_c0 = true;
config.logpower = 1;
config.window = RectangularWindow; // because no overlap
m_rhythmfcc = new MFCC(config);
}
for (int i = 0; i < m_channels; ++i) {
m_values.push_back(FeatureMatrix());
if (needRhythm()) {
m_rhythmValues.push_back(FeatureColumnQueue());
}
}
m_done = false;
return true;
}
void
SimilarityPlugin::reset()
{
for (int i = 0; i < int(m_values.size()); ++i) {
m_values[i].clear();
}
for (int i = 0; i < int(m_rhythmValues.size()); ++i) {
m_rhythmValues[i].clear();
}
for (int i = 0; i < int(m_lastNonEmptyFrame.size()); ++i) {
m_lastNonEmptyFrame[i] = -1;
}
for (int i = 0; i < int(m_emptyFrameCount.size()); ++i) {
m_emptyFrameCount[i] = 0;
}
m_done = false;
}
SimilarityPlugin::FeatureSet
SimilarityPlugin::process(const float *const *inputBuffers, Vamp::RealTime /* timestamp */)
{
if (m_done) {
return FeatureSet();
}
double *dblbuf = new double[m_blockSize];
double *decbuf = dblbuf;
if (m_decimator) decbuf = new double[m_fftSize];
double *raw = new double[std::max(m_featureColumnSize,
m_rhythmColumnSize)];
float threshold = 1e-10;
bool someRhythmFrameNeeded = false;
for (int c = 0; c < m_channels; ++c) {
bool empty = true;
for (int i = 0; i < m_blockSize; ++i) {
float val = inputBuffers[c][i];
if (fabs(val) > threshold) empty = false;
dblbuf[i] = val;
}
if (empty) {
if (needRhythm() && ((m_frameNo % 2) == 0)) {
for (int i = 0; i < m_fftSize / m_rhythmClipFrameSize; ++i) {
if (int(m_rhythmValues[c].size()) < m_rhythmClipFrames) {
FeatureColumn mf(m_rhythmColumnSize);
for (int i = 0; i < m_rhythmColumnSize; ++i) {
mf[i] = 0.0;
}
m_rhythmValues[c].push_back(mf);
}
}
}
m_emptyFrameCount[c]++;
continue;
}
m_lastNonEmptyFrame[c] = m_frameNo;
if (m_decimator) {
m_decimator->process(dblbuf, decbuf);
}
if (needTimbre()) {
FeatureColumn mf(m_featureColumnSize);
if (m_type == TypeMFCC) {
m_mfcc->process(decbuf, raw);
for (int i = 0; i < m_featureColumnSize; ++i) {
mf[i] = raw[i];
}
} else if (m_type == TypeChroma) {
double *chroma = m_chromagram->process(decbuf);
for (int i = 0; i < m_featureColumnSize; ++i) {
mf[i] = chroma[i];
}
}
m_values[c].push_back(mf);
}
// std::cerr << "needRhythm = " << needRhythm() << ", frame = " << m_frameNo << std::endl;
if (needRhythm() && ((m_frameNo % 2) == 0)) {
// The incoming frames are overlapping; we only use every
// other one, because we don't want the overlap (it would
// screw up the rhythm)
int frameOffset = 0;
while (frameOffset + m_rhythmClipFrameSize <= m_fftSize) {
bool needRhythmFrame = true;
if (int(m_rhythmValues[c].size()) >= m_rhythmClipFrames) {
needRhythmFrame = false;
// assumes hopsize = framesize/2
float current = m_frameNo * (m_fftSize/2) + frameOffset;
current = current / m_processRate;
if (current - m_rhythmClipDuration < m_rhythmClipOrigin) {
needRhythmFrame = true;
m_rhythmValues[c].pop_front();
}
// if (needRhythmFrame) {
// std::cerr << "at current = " <<current << " (frame = " << m_frameNo << "), have " << m_rhythmValues[c].size() << ", need rhythm = " << needRhythmFrame << std::endl;
// }
}
if (needRhythmFrame) {
someRhythmFrameNeeded = true;
m_rhythmfcc->process(decbuf + frameOffset, raw);
FeatureColumn mf(m_rhythmColumnSize);
for (int i = 0; i < m_rhythmColumnSize; ++i) {
mf[i] = raw[i];
}
m_rhythmValues[c].push_back(mf);
}
frameOffset += m_rhythmClipFrameSize;
}
}
}
if (!needTimbre() && !someRhythmFrameNeeded && ((m_frameNo % 2) == 0)) {
// std::cerr << "done!" << std::endl;
m_done = true;
}
if (m_decimator) delete[] decbuf;
delete[] dblbuf;
delete[] raw;
++m_frameNo;
return FeatureSet();
}
SimilarityPlugin::FeatureMatrix
SimilarityPlugin::calculateTimbral(FeatureSet &returnFeatures)
{
FeatureMatrix m(m_channels); // means
FeatureMatrix v(m_channels); // variances
for (int i = 0; i < m_channels; ++i) {
FeatureColumn mean(m_featureColumnSize), variance(m_featureColumnSize);
for (int j = 0; j < m_featureColumnSize; ++j) {
mean[j] = 0.0;
variance[j] = 0.0;
int count;
// We want to take values up to, but not including, the
// last non-empty frame (which may be partial)
int sz = m_lastNonEmptyFrame[i] - m_emptyFrameCount[i];
if (sz < 0) sz = 0;
if (sz >= int(m_values[i].size())) {
sz = int(m_values[i].size())-1;
}
count = 0;
for (int k = 0; k < sz; ++k) {
double val = m_values[i][k][j];
if (ISNAN(val) || ISINF(val)) continue;
mean[j] += val;
++count;
}
if (count > 0) mean[j] /= count;
count = 0;
for (int k = 0; k < sz; ++k) {
double val = ((m_values[i][k][j] - mean[j]) *
(m_values[i][k][j] - mean[j]));
if (ISNAN(val) || ISINF(val)) continue;
variance[j] += val;
++count;
}
if (count > 0) variance[j] /= count;
}
m[i] = mean;
v[i] = variance;
}
FeatureMatrix distances(m_channels);
if (m_type == TypeMFCC) {
// "Despite the fact that MFCCs extracted from music are
// clearly not Gaussian, [14] showed, somewhat surprisingly,
// that a similarity function comparing single Gaussians
// modelling MFCCs for each track can perform as well as
// mixture models. A great advantage of using single
// Gaussians is that a simple closed form exists for the KL
// divergence." -- Mark Levy, "Lightweight measures for
// timbral similarity of musical audio"
// (http://www.elec.qmul.ac.uk/easaier/papers/mlevytimbralsimilarity.pdf)
KLDivergence kld;
for (int i = 0; i < m_channels; ++i) {
for (int j = 0; j < m_channels; ++j) {
double d = kld.distanceGaussian(m[i], v[i], m[j], v[j]);
distances[i].push_back(d);
}
}
} else {
// We use the KL divergence for distributions of discrete
// variables, as chroma are histograms already. Or at least,
// they will be when we've normalised them like this:
for (int i = 0; i < m_channels; ++i) {
MathUtilities::normalise(m[i], MathUtilities::NormaliseUnitSum);
}
KLDivergence kld;
for (int i = 0; i < m_channels; ++i) {
for (int j = 0; j < m_channels; ++j) {
double d = kld.distanceDistribution(m[i], m[j], true);
distances[i].push_back(d);
}
}
}
Feature feature;
feature.hasTimestamp = true;
char labelBuffer[100];
for (int i = 0; i < m_channels; ++i) {
feature.timestamp = Vamp::RealTime(i, 0);
sprintf(labelBuffer, "Means for channel %d", i+1);
feature.label = labelBuffer;
feature.values.clear();
for (int k = 0; k < m_featureColumnSize; ++k) {
feature.values.push_back(m[i][k]);
}
returnFeatures[m_meansOutput].push_back(feature);
sprintf(labelBuffer, "Variances for channel %d", i+1);
feature.label = labelBuffer;
feature.values.clear();
for (int k = 0; k < m_featureColumnSize; ++k) {
feature.values.push_back(v[i][k]);
}
returnFeatures[m_variancesOutput].push_back(feature);
}
return distances;
}
SimilarityPlugin::FeatureMatrix
SimilarityPlugin::calculateRhythmic(FeatureSet &returnFeatures)
{
if (!needRhythm()) return FeatureMatrix();
// std::cerr << "SimilarityPlugin::initialise: rhythm clip for channel 0 contains "
// << m_rhythmValues[0].size() << " frames of size "
// << m_rhythmClipFrameSize << " at process rate "
// << m_processRate << " ( = "
// << (float(m_rhythmValues[0].size() * m_rhythmClipFrameSize) / m_processRate) << " sec )"
// << std::endl;
BeatSpectrum bscalc;
CosineDistance cd;
// Our rhythm feature matrix is a deque of vectors for practical
// reasons, but BeatSpectrum::process wants a vector of vectors
// (which is what FeatureMatrix happens to be).
FeatureMatrixSet bsinput(m_channels);
for (int i = 0; i < m_channels; ++i) {
for (int j = 0; j < int(m_rhythmValues[i].size()); ++j) {
bsinput[i].push_back(m_rhythmValues[i][j]);
}
}
FeatureMatrix bs(m_channels);
for (int i = 0; i < m_channels; ++i) {
bs[i] = bscalc.process(bsinput[i]);
}
FeatureMatrix distances(m_channels);
for (int i = 0; i < m_channels; ++i) {
for (int j = 0; j < m_channels; ++j) {
double d = cd.distance(bs[i], bs[j]);
distances[i].push_back(d);
}
}
Feature feature;
feature.hasTimestamp = true;
char labelBuffer[100];
for (int i = 0; i < m_channels; ++i) {
feature.timestamp = Vamp::RealTime(i, 0);
sprintf(labelBuffer, "Beat spectrum for channel %d", i+1);
feature.label = labelBuffer;
feature.values.clear();
for (int j = 0; j < int(bs[i].size()); ++j) {
feature.values.push_back(bs[i][j]);
}
returnFeatures[m_beatSpectraOutput].push_back(feature);
}
return distances;
}
double
SimilarityPlugin::getDistance(const FeatureMatrix &timbral,
const FeatureMatrix &rhythmic,
int i, int j)
{
double distance = 1.0;
if (needTimbre()) distance *= timbral[i][j];
if (needRhythm()) distance *= rhythmic[i][j];
return distance;
}
SimilarityPlugin::FeatureSet
SimilarityPlugin::getRemainingFeatures()
{
FeatureSet returnFeatures;
// We want to return a matrix of the distances between channels,
// but Vamp doesn't have a matrix return type so we will actually
// return a series of vectors
FeatureMatrix timbralDistances, rhythmicDistances;
if (needTimbre()) {
timbralDistances = calculateTimbral(returnFeatures);
}
if (needRhythm()) {
rhythmicDistances = calculateRhythmic(returnFeatures);
}
// We give all features a timestamp, otherwise hosts will tend to
// stamp them at the end of the file, which is annoying
Feature feature;
feature.hasTimestamp = true;
Feature distanceVectorFeature;
distanceVectorFeature.label = "Distance from first channel";
distanceVectorFeature.hasTimestamp = true;
distanceVectorFeature.timestamp = Vamp::RealTime::zeroTime;
std::map<double, int> sorted;
char labelBuffer[100];
for (int i = 0; i < m_channels; ++i) {
feature.timestamp = Vamp::RealTime(i, 0);
feature.values.clear();
for (int j = 0; j < m_channels; ++j) {
double dist = getDistance(timbralDistances, rhythmicDistances, i, j);
feature.values.push_back(dist);
}
sprintf(labelBuffer, "Distances from channel %d", i+1);
feature.label = labelBuffer;
returnFeatures[m_distanceMatrixOutput].push_back(feature);
double fromFirst =
getDistance(timbralDistances, rhythmicDistances, 0, i);
distanceVectorFeature.values.push_back(fromFirst);
sorted[fromFirst] = i;
}
returnFeatures[m_distanceVectorOutput].push_back(distanceVectorFeature);
feature.label = "Order of channels by similarity to first channel";
feature.values.clear();
feature.timestamp = Vamp::RealTime(0, 0);
for (std::map<double, int>::iterator i = sorted.begin();
i != sorted.end(); ++i) {
feature.values.push_back(i->second + 1);
}
returnFeatures[m_sortedVectorOutput].push_back(feature);
feature.label = "Ordered distances of channels from first channel";
feature.values.clear();
feature.timestamp = Vamp::RealTime(1, 0);
for (std::map<double, int>::iterator i = sorted.begin();
i != sorted.end(); ++i) {
feature.values.push_back(i->first);
}
returnFeatures[m_sortedVectorOutput].push_back(feature);
return returnFeatures;
}