view examples/FixedTempoEstimator.cpp @ 209:0f6616ef0e18

* More tweaks (still incomplete)

/* -*- 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_t(0),
    m_n(0)
{
}

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

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 64;
}

size_t
FixedTempoEstimator::getPreferredBlockSize() const
{
    return 256;
}

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 = 10.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()
{
    cerr << "FixedTempoEstimator: reset called" << endl;

    if (!m_priorMagnitudes) return;

    cerr << "FixedTempoEstimator: resetting" << 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;

    delete[] m_t; 
    m_t = 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) cerr << "m_n = " << m_n << 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();

    float value = 0.f;

    bool print = (ts == RealTime::zeroTime);

    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]);

        if (i == 1 && ts == RealTime::zeroTime) {
            cerr << "First sqrmag: " << sqrmag << ", value = " << value << endl;
        }

        m_priorMagnitudes[i] = sqrmag;
    }

    m_df[m_n] = value;

    ++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);
}

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

void
FixedTempoEstimator::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) {
        cerr << "FixedTempoEstimator::calculate: Not enough data to go on (have " << m_n << ", want at least " << m_dfsize/4 << ")" << 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];
    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;
    }
/*
    for (int i = 1; i < n/2; ++i) {

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

        cerr << "i = " << i << ": tempo = " << lag2tempo(i) << ", weight = " << weight << ", " << m_r[i] << " -> ";

        m_fr[i] = m_r[i] * weight; //(1 + weight / 2.f);
        
        cerr << m_fr[i] << endl;
    }
*/
    int related[] = { 2, 3, 4 };

    int e = tempo2lag(50.f);
//    int universalDiv = (n/2 - 1) / e;
//    cerr << "universalDiv = " << universalDiv << endl;

    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;

//        cerr << "i = " << i << ": tempo = " << lag2tempo(i) << ", weight = " << weight << ", " << m_r[i] << " -> ";

        m_fr[i] = m_r[i];
//        m_fr[i] = m_r[i] * weight; //(1 + weight / 2.f);
        
//        if (i == 0 || i == n/2 - 1
/*
            ||
            !(m_fr[i] > m_fr[i-1] &&
              m_fr[i] >= m_fr[i+1])
*/
//            ) {
//            continue;
//        }

        int div = 1;

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

            int k0 = i * related[j];

            if (k0 >= 0 && k0 < 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] / 4;

//                if (related[j] <= universalDiv) {
//                m_fr[i] += m_fr[kmax]; //!!!
//                    m_fr[i] += m_r[kmax] / related[j];
//                }

                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) {
                    
//                    cerr << "peak at " << i << " (val " << m_r[i] << ", tempo " << lag2tempo(i) << ") has sympathetic peak at " << kmax << " (val " << m_r[kmax] << " for relative tempo " << lag2tempo(kmax) * related[j] << ")" << endl;

                    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 / 5);
    }

/*
    int e = tempo2lag(60.f);
    int div = (n/2 - 1) / e;

//    cerr << "e = " << e << ", n/2 = " << n/2 << ", div = " << div << endl;
    if (div > 1) {
        for (int j = 2; j <= div && j <= 8; j *= 2) {
            for (int i = 1; i <= e; ++i) {
                m_fr[i] += m_fr[i * j] * (1.f / j);
            }
        }
    }
*/
//        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) << endl;


//    }

    cerr << "FixedTempoEstimator::calculate done" << 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 = 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 = 50.f;
    float t1 = 190.f;

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

    cerr << "p0 = " << p0 << ", p1 = " << p1 << endl;

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

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

    std::map<float, int> candidates;

    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) {
        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;
}