view examples/FixedTempoEstimator.cpp @ 201:8e1b1fa94e15

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/* -*- 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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*/

#include "FixedTempoEstimator.h"

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

using Vamp::RealTime;

#include <cmath>


FixedTempoEstimator::FixedTempoEstimator(float inputSampleRate) :
    Plugin(inputSampleRate),
    m_stepSize(0),
    m_blockSize(0),
    m_priorMagnitudes(0),
    m_df(0),
    m_r(0),
    m_fr(0),
    m_n(0)
{
}

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

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 0;
}

size_t
FixedTempoEstimator::getPreferredBlockSize() const
{
    return 64;
}

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

    m_stepSize = stepSize;
    m_blockSize = blockSize;

    float dfLengthSecs = 8.f;
    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::reset()
{
    std::cerr << "FixedTempoEstimator: reset called" << std::endl;

    if (!m_priorMagnitudes) return;

    std::cerr << "FixedTempoEstimator: resetting" << std::endl;

    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;

    m_n = 0;

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

FixedTempoEstimator::ParameterList
FixedTempoEstimator::getParameterDescriptors() const
{
    ParameterList list;
    return list;
}

float
FixedTempoEstimator::getParameter(std::string id) const
{
    return 0.f;
}

void
FixedTempoEstimator::setParameter(std::string id, float value)
{
}

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

FixedTempoEstimator::OutputList
FixedTempoEstimator::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;
}

FixedTempoEstimator::FeatureSet
FixedTempoEstimator::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 < m_dfsize) std::cerr << "m_n = " << m_n << std::endl;

    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();

    int count = 0;

    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;

        if (m_priorMagnitudes[i] > 0.f) {
            float diff = 10.f * log10f(sqrmag / m_priorMagnitudes[i]);
            if (diff >= 3.f) ++count;
        }

        m_priorMagnitudes[i] = sqrmag;
    }

    m_df[m_n] = float(count) / float(m_blockSize/2);
    ++m_n;
    return fs;
}

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

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

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

    if (m_n < m_dfsize / 6) {
        std::cerr << "FixedTempoEstimator::calculate: Not enough data to go on (have " << m_n << ", want at least " << m_dfsize/4 << ")" << std::endl;
        return; // not enough data (perhaps we should return the duration of the input as the "estimated" beat length?)
    }

    int n = m_n;

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

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

    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;
    }

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

        m_fr[i] = m_r[i];

        int div = 1;

        int j = i;
        
        while (j < n/2) {
            m_fr[i] += m_r[j];
            j *= 2;
            ++div;
        }
/*
        for (int j = 1; j <= (n/2 - 1)/i; ++j) {
            m_fr[i] += m_r[i * j];
            ++div;
        }
*/
        std::cerr << "i = " << i << ", (n/2 - 1)/i = " << (n/2 - 1)/i << ", sum = " << m_fr[i] << ", div = " << div << ", val = " << m_fr[i] / div << ", t = " << lag2tempo(i) << std::endl;


//        m_fr[i] /= 1 + (n/2 - 1)/i;
        m_fr[i] /= div;
    }

    std::cerr << "FixedTempoEstimator::calculate done" << std::endl;
}
    

FixedTempoEstimator::FeatureSet
FixedTempoEstimator::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 = 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 = 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 = 60.f;
    float t1 = 180.f;

    int p0 = ((60.f / t1) * m_inputSampleRate) / m_stepSize;
    int p1 = ((60.f / t0) * m_inputSampleRate) / m_stepSize;

//    std::cerr << "p0 = " << p0 << ", p1 = " << p1 << std::endl;

    int pc = p1 - p0 + 1;
//    std::cerr << "pc = " << pc << std::endl;

//    int maxpi = 0;
//    float maxp = 0.f;

    std::map<float, int> candidates;

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

        // Only candidates here are those that were peaks in the
        // original acf
//        if (r[i] > r[i-1] && r[i] > r[i+1]) {
//            candidates[filtered] = i;
//        }

        candidates[m_fr[i]] = i;

        feature.timestamp = 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);
    }

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

    if (candidates.empty()) {
        std::cerr << "No tempo candidates!" << std::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;
    
    feature.values[0] = lag2tempo(maxpi);

    sprintf(buffer, "%.1f bpm", lag2tempo(maxpi));
    feature.label = buffer;

    fs[TempoOutput].push_back(feature);

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

    while (feature.values.size() < 8) {
        feature.values.push_back(lag2tempo(ci->second));
        if (ci == candidates.begin()) break;
        --ci;
    }

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