view examples/FixedTempoEstimator.cpp @ 243:3cf5bd155e5b

* Some build improvements
* Make parameterisable values in tempo estimator into parameters

/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */

/*
    Vamp

    An API for audio analysis and feature extraction plugins.

    Centre for Digital Music, Queen Mary, University of London.
    Copyright 2006-2008 Chris Cannam and QMUL.
  
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    obtaining a copy of this software and associated documentation
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*/

#include "FixedTempoEstimator.h"

using std::string;
using std::vector;
using std::cerr;
using std::endl;

using Vamp::RealTime;

#include <cmath>


class FixedTempoEstimator::D
{
public:
    D(float inputSampleRate);
    ~D();

    size_t getPreferredStepSize() const { return 64; }
    size_t getPreferredBlockSize() const { return 256; }

    ParameterList getParameterDescriptors() const;
    float getParameter(string id) const;
    void setParameter(string id, float value);

    OutputList getOutputDescriptors() const;

    bool initialise(size_t channels, size_t stepSize, size_t blockSize);
    void reset();
    FeatureSet process(const float *const *, RealTime);
    FeatureSet getRemainingFeatures();

private:
    void calculate();
    FeatureSet assembleFeatures();

    float lag2tempo(int);
    int tempo2lag(float);

    float m_inputSampleRate;
    size_t m_stepSize;
    size_t m_blockSize;

    float m_minbpm;
    float m_maxbpm;
    float m_maxdflen;

    float *m_priorMagnitudes;

    size_t m_dfsize;
    float *m_df;
    float *m_r;
    float *m_fr;
    float *m_t;
    size_t m_n;

    Vamp::RealTime m_start;
    Vamp::RealTime m_lasttime;
};

FixedTempoEstimator::D::D(float inputSampleRate) :
    m_inputSampleRate(inputSampleRate),
    m_stepSize(0),
    m_blockSize(0),
    m_minbpm(50),
    m_maxbpm(190),
    m_maxdflen(10),
    m_priorMagnitudes(0),
    m_df(0),
    m_r(0),
    m_fr(0),
    m_t(0),
    m_n(0)
{
}

FixedTempoEstimator::D::~D()
{
    delete[] m_priorMagnitudes;
    delete[] m_df;
    delete[] m_r;
    delete[] m_fr;
    delete[] m_t;
}

FixedTempoEstimator::ParameterList
FixedTempoEstimator::D::getParameterDescriptors() const
{
    ParameterList list;

    ParameterDescriptor d;
    d.identifier = "minbpm";
    d.name = "Minimum estimated tempo";
    d.description = "Minimum beat-per-minute value which the tempo estimator is able to return";
    d.unit = "bpm";
    d.minValue = 10;
    d.maxValue = 360;
    d.defaultValue = 50;
    d.isQuantized = false;
    list.push_back(d);

    d.identifier = "maxbpm";
    d.name = "Maximum estimated tempo";
    d.description = "Maximum beat-per-minute value which the tempo estimator is able to return";
    d.defaultValue = 190;
    list.push_back(d);

    d.identifier = "maxdflen";
    d.name = "Input duration to study";
    d.description = "Length of audio input, in seconds, which should be taken into account when estimating tempo.  There is no need to supply the plugin with any further input once this time has elapsed since the start of the audio.  The tempo estimator may use only the first part of this, up to eight times the slowest beat duration: increasing this value further than that is unlikely to improve results.";
    d.unit = "s";
    d.minValue = 2;
    d.maxValue = 40;
    d.defaultValue = 10;
    list.push_back(d);

    return list;
}

float
FixedTempoEstimator::D::getParameter(string id) const
{
    if (id == "minbpm") {
        return m_minbpm;
    } else if (id == "maxbpm") {
        return m_maxbpm;
    } else if (id == "maxdflen") {
        return m_maxdflen;
    }
    return 0.f;
}

void
FixedTempoEstimator::D::setParameter(string id, float value)
{
    if (id == "minbpm") {
        m_minbpm = value;
    } else if (id == "maxbpm") {
        m_maxbpm = value;
    } else if (id == "maxdflen") {
        m_maxdflen = value;
    }
}

static int TempoOutput = 0;
static int CandidatesOutput = 1;
static int DFOutput = 2;
static int ACFOutput = 3;
static int FilteredACFOutput = 4;

FixedTempoEstimator::OutputList
FixedTempoEstimator::D::getOutputDescriptors() const
{
    OutputList list;

    OutputDescriptor d;
    d.identifier = "tempo";
    d.name = "Tempo";
    d.description = "Estimated tempo";
    d.unit = "bpm";
    d.hasFixedBinCount = true;
    d.binCount = 1;
    d.hasKnownExtents = false;
    d.isQuantized = false;
    d.sampleType = OutputDescriptor::VariableSampleRate;
    d.sampleRate = m_inputSampleRate;
    d.hasDuration = true; // our returned tempo spans a certain range
    list.push_back(d);

    d.identifier = "candidates";
    d.name = "Tempo candidates";
    d.description = "Possible tempo estimates, one per bin with the most likely in the first bin";
    d.unit = "bpm";
    d.hasFixedBinCount = false;
    list.push_back(d);

    d.identifier = "detectionfunction";
    d.name = "Detection Function";
    d.description = "Onset detection function";
    d.unit = "";
    d.hasFixedBinCount = 1;
    d.binCount = 1;
    d.hasKnownExtents = true;
    d.minValue = 0.0;
    d.maxValue = 1.0;
    d.isQuantized = false;
    d.quantizeStep = 0.0;
    d.sampleType = OutputDescriptor::FixedSampleRate;
    if (m_stepSize) {
        d.sampleRate = m_inputSampleRate / m_stepSize;
    } else {
        d.sampleRate = m_inputSampleRate / (getPreferredBlockSize()/2);
    }
    d.hasDuration = false;
    list.push_back(d);

    d.identifier = "acf";
    d.name = "Autocorrelation Function";
    d.description = "Autocorrelation of onset detection function";
    d.hasKnownExtents = false;
    d.unit = "r";
    list.push_back(d);

    d.identifier = "filtered_acf";
    d.name = "Filtered Autocorrelation";
    d.description = "Filtered autocorrelation of onset detection function";
    d.unit = "r";
    list.push_back(d);

    return list;
}

bool
FixedTempoEstimator::D::initialise(size_t channels,
                                   size_t stepSize, size_t blockSize)
{
    m_stepSize = stepSize;
    m_blockSize = blockSize;

    float dfLengthSecs = m_maxdflen;
    m_dfsize = (dfLengthSecs * m_inputSampleRate) / m_stepSize;

    m_priorMagnitudes = new float[m_blockSize/2];
    m_df = new float[m_dfsize];

    for (size_t i = 0; i < m_blockSize/2; ++i) {
        m_priorMagnitudes[i] = 0.f;
    }
    for (size_t i = 0; i < m_dfsize; ++i) {
        m_df[i] = 0.f;
    }

    m_n = 0;

    return true;
}

void
FixedTempoEstimator::D::reset()
{
    if (!m_priorMagnitudes) return;

    for (size_t i = 0; i < m_blockSize/2; ++i) {
        m_priorMagnitudes[i] = 0.f;
    }
    for (size_t i = 0; i < m_dfsize; ++i) {
        m_df[i] = 0.f;
    }

    delete[] m_r;
    m_r = 0;

    delete[] m_fr; 
    m_fr = 0;

    delete[] m_t; 
    m_t = 0;

    m_n = 0;

    m_start = RealTime::zeroTime;
    m_lasttime = RealTime::zeroTime;
}

FixedTempoEstimator::FeatureSet
FixedTempoEstimator::D::process(const float *const *inputBuffers, RealTime ts)
{
    FeatureSet fs;

    if (m_stepSize == 0) {
	cerr << "ERROR: FixedTempoEstimator::process: "
	     << "FixedTempoEstimator has not been initialised"
	     << endl;
	return fs;
    }

    if (m_n == 0) m_start = ts;
    m_lasttime = ts;

    if (m_n == m_dfsize) {
        calculate();
        fs = assembleFeatures();
        ++m_n;
        return fs;
    }

    if (m_n > m_dfsize) return FeatureSet();

    float value = 0.f;

    for (size_t i = 1; i < m_blockSize/2; ++i) {

        float real = inputBuffers[0][i*2];
        float imag = inputBuffers[0][i*2 + 1];

        float sqrmag = real * real + imag * imag;
        value += fabsf(sqrmag - m_priorMagnitudes[i]);

        m_priorMagnitudes[i] = sqrmag;
    }

    m_df[m_n] = value;

    ++m_n;
    return fs;
}    

FixedTempoEstimator::FeatureSet
FixedTempoEstimator::D::getRemainingFeatures()
{
    FeatureSet fs;
    if (m_n > m_dfsize) return fs;
    calculate();
    fs = assembleFeatures();
    ++m_n;
    return fs;
}

float
FixedTempoEstimator::D::lag2tempo(int lag)
{
    return 60.f / ((lag * m_stepSize) / m_inputSampleRate);
}

int
FixedTempoEstimator::D::tempo2lag(float tempo)
{
    return ((60.f / tempo) * m_inputSampleRate) / m_stepSize;
}

void
FixedTempoEstimator::D::calculate()
{    
    cerr << "FixedTempoEstimator::calculate: m_n = " << m_n << endl;
    
    if (m_r) {
        cerr << "FixedTempoEstimator::calculate: calculation already happened?" << endl;
        return;
    }

    if (m_n < m_dfsize / 9 &&
        m_n < (1.0 * m_inputSampleRate) / m_stepSize) { // 1 second
        cerr << "FixedTempoEstimator::calculate: Input is too short" << endl;
        return;
    }

    int n = m_n;

    m_r = new float[n/2];
    m_fr = new float[n/2];
    m_t = new float[n/2];

    for (int i = 0; i < n/2; ++i) {
        m_r[i] = 0.f;
        m_fr[i] = 0.f;
        m_t[i] = lag2tempo(i);
    }

    for (int i = 0; i < n/2; ++i) {

        for (int j = i; j < n-1; ++j) {
            m_r[i] += m_df[j] * m_df[j - i];
        }

        m_r[i] /= n - i - 1;
    }

    float related[] = { 0.5, 2, 3, 4 };

    for (int i = 1; i < n/2-1; ++i) {

        float weight = 1.f - fabsf(128.f - lag2tempo(i)) * 0.005;
        if (weight < 0.f) weight = 0.f;
        weight = weight * weight * weight;

        m_fr[i] = m_r[i];

        int div = 1;

        for (int j = 0; j < int(sizeof(related)/sizeof(related[0])); ++j) {

            int k0 = int(i * related[j] + 0.5);

            if (k0 >= 0 && k0 < int(n/2)) {

                int kmax = 0, kmin = 0;
                float kvmax = 0, kvmin = 0;
                bool have = false;

                for (int k = k0 - 1; k <= k0 + 1; ++k) {

                    if (k < 0 || k >= n/2) continue;

                    if (!have || (m_r[k] > kvmax)) { kmax = k; kvmax = m_r[k]; }
                    if (!have || (m_r[k] < kvmin)) { kmin = k; kvmin = m_r[k]; }
                    
                    have = true;
                }
                
                m_fr[i] += m_r[kmax] / 5;

                if ((kmax == 0 || m_r[kmax] > m_r[kmax-1]) &&
                    (kmax == n/2-1 || m_r[kmax] > m_r[kmax+1]) &&
                    kvmax > kvmin * 1.05) {
                    
                    m_t[i] = m_t[i] + lag2tempo(kmax) * related[j];
                    ++div;
                }
            }
        }
        
        m_t[i] /= div;
        
//        if (div > 1) {
//            cerr << "adjusting tempo from " << lag2tempo(i) << " to "
//                 << m_t[i] << " for fr = " << m_fr[i] << " (div = " << div << ")" << endl;
//        }
        
        m_fr[i] += m_fr[i] * (weight / 3);
    }
}
    
FixedTempoEstimator::FeatureSet
FixedTempoEstimator::D::assembleFeatures()
{
    FeatureSet fs;
    if (!m_r) return fs; // No results

    Feature feature;
    feature.hasTimestamp = true;
    feature.hasDuration = false;
    feature.label = "";
    feature.values.clear();
    feature.values.push_back(0.f);

    char buffer[40];

    int n = m_n;

    for (int i = 0; i < n; ++i) {
        feature.timestamp = m_start +
            RealTime::frame2RealTime(i * m_stepSize, m_inputSampleRate);
        feature.values[0] = m_df[i];
        feature.label = "";
        fs[DFOutput].push_back(feature);
    }

    for (int i = 1; i < n/2; ++i) {
        feature.timestamp = m_start +
            RealTime::frame2RealTime(i * m_stepSize, m_inputSampleRate);
        feature.values[0] = m_r[i];
        sprintf(buffer, "%.1f bpm", lag2tempo(i));
        if (i == n/2-1) feature.label = "";
        else feature.label = buffer;
        fs[ACFOutput].push_back(feature);
    }

    float t0 = m_minbpm; // our minimum detected tempo
    float t1 = m_maxbpm; // our maximum detected tempo

    int p0 = tempo2lag(t1);
    int p1 = tempo2lag(t0);

    std::map<float, int> candidates;

    std::cerr << "minbpm = " << m_minbpm << ", p0 = " << p0 << ", p1 = " << p1 << std::endl;

    for (int i = p0; i <= p1 && i < n/2-1; ++i) {

        if (m_fr[i] > m_fr[i-1] &&
            m_fr[i] > m_fr[i+1]) {
            candidates[m_fr[i]] = i;
        }

        feature.timestamp = m_start +
            RealTime::frame2RealTime(i * m_stepSize, m_inputSampleRate);
        feature.values[0] = m_fr[i];
        sprintf(buffer, "%.1f bpm", lag2tempo(i));
        if (i == p1 || i == n/2-2) feature.label = "";
        else feature.label = buffer;
        fs[FilteredACFOutput].push_back(feature);
    }

//    cerr << "maxpi = " << maxpi << " for tempo " << lag2tempo(maxpi) << " (value = " << maxp << ")" << endl;

    if (candidates.empty()) {
        cerr << "No tempo candidates!" << endl;
        return fs;
    }

    feature.hasTimestamp = true;
    feature.timestamp = m_start;
    
    feature.hasDuration = true;
    feature.duration = m_lasttime - m_start;

    std::map<float, int>::const_iterator ci = candidates.end();
    --ci;
    int maxpi = ci->second;

    if (m_t[maxpi] > 0) {
        cerr << "*** Using adjusted tempo " << m_t[maxpi] << " instead of lag tempo " << lag2tempo(maxpi) << endl;
        feature.values[0] = m_t[maxpi];
    } else {
        // shouldn't happen -- it would imply that this high value was not a peak!
        feature.values[0] = lag2tempo(maxpi);
        cerr << "WARNING: No stored tempo for index " << maxpi << endl;
    }

    sprintf(buffer, "%.1f bpm", feature.values[0]);
    feature.label = buffer;

    fs[TempoOutput].push_back(feature);

    feature.values.clear();
    feature.label = "";

    while (feature.values.size() < 8) {
//        cerr << "adding tempo value from lag " << ci->second << endl;
        if (m_t[ci->second] > 0) {
            feature.values.push_back(m_t[ci->second]);
        } else {
            feature.values.push_back(lag2tempo(ci->second));
        }
        if (ci == candidates.begin()) break;
        --ci;
    }

    fs[CandidatesOutput].push_back(feature);
    
    return fs;
}

    

FixedTempoEstimator::FixedTempoEstimator(float inputSampleRate) :
    Plugin(inputSampleRate),
    m_d(new D(inputSampleRate))
{
}

FixedTempoEstimator::~FixedTempoEstimator()
{
}

string
FixedTempoEstimator::getIdentifier() const
{
    return "fixedtempo";
}

string
FixedTempoEstimator::getName() const
{
    return "Simple Fixed Tempo Estimator";
}

string
FixedTempoEstimator::getDescription() const
{
    return "Study a short section of audio and estimate its tempo, assuming the tempo is constant";
}

string
FixedTempoEstimator::getMaker() const
{
    return "Vamp SDK Example Plugins";
}

int
FixedTempoEstimator::getPluginVersion() const
{
    return 1;
}

string
FixedTempoEstimator::getCopyright() const
{
    return "Code copyright 2008 Queen Mary, University of London.  Freely redistributable (BSD license)";
}

size_t
FixedTempoEstimator::getPreferredStepSize() const
{
    return m_d->getPreferredStepSize();
}

size_t
FixedTempoEstimator::getPreferredBlockSize() const
{
    return m_d->getPreferredBlockSize();
}

bool
FixedTempoEstimator::initialise(size_t channels, size_t stepSize, size_t blockSize)
{
    if (channels < getMinChannelCount() ||
	channels > getMaxChannelCount()) return false;

    return m_d->initialise(channels, stepSize, blockSize);
}

void
FixedTempoEstimator::reset()
{
    return m_d->reset();
}

FixedTempoEstimator::ParameterList
FixedTempoEstimator::getParameterDescriptors() const
{
    return m_d->getParameterDescriptors();
}

float
FixedTempoEstimator::getParameter(std::string id) const
{
    return m_d->getParameter(id);
}

void
FixedTempoEstimator::setParameter(std::string id, float value)
{
    m_d->setParameter(id, value);
}

FixedTempoEstimator::OutputList
FixedTempoEstimator::getOutputDescriptors() const
{
    return m_d->getOutputDescriptors();
}

FixedTempoEstimator::FeatureSet
FixedTempoEstimator::process(const float *const *inputBuffers, RealTime ts)
{
    return m_d->process(inputBuffers, ts);
}

FixedTempoEstimator::FeatureSet
FixedTempoEstimator::getRemainingFeatures()
{
    return m_d->getRemainingFeatures();
}