comparison examples/FixedTempoEstimator.cpp @ 227:6b30e064cab7 distinct-libraries

* more moving

+/* -*- 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.
+  
+    Permission is hereby granted, free of charge, to any person
+    obtaining a copy of this software and associated documentation
+    files (the "Software"), to deal in the Software without
+    restriction, including without limitation the rights to use, copy,
+    modify, merge, publish, distribute, sublicense, and/or sell copies
+    of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be
+    included in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+    NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR
+    ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
+    CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+    WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+    Except as contained in this notice, the names of the Centre for
+    Digital Music; Queen Mary, University of London; and Chris Cannam
+    shall not be used in advertising or otherwise to promote the sale,
+    use or other dealings in this Software without prior written
+    authorization.
+*/
+
+#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;
+
+    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::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;
+    }
+
+    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::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; // our minimum detected tempo (could be a parameter)
+    float t1 = 190.f; // our maximum detected tempo
+
+    //!!! need some way for the host (or at least, the user) to know
+    //!!! that it should only pass a certain amount of
+    //!!! input... e.g. by making the amount configurable
+
+    int p0 = tempo2lag(t1);
+    int p1 = tempo2lag(t0);
+
+    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) {
+//        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;
+}