diff examples/FixedTempoEstimator.cpp @ 198:e3e61b7e9661

* Beginnings of a simple tempo estimator example plugin

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/examples/FixedTempoEstimator.cpp	Wed Oct 08 15:26:50 2008 +0000
@@ -0,0 +1,426 @@
+/* -*- 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)
+{
+}
+
+FixedTempoEstimator::~FixedTempoEstimator()
+{
+    delete[] m_priorMagnitudes;
+    delete[] m_df;
+}
+
+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 128;
+}
+
+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;
+    }
+
+    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)
+{
+}
+
+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 = "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;
+    list.push_back(d);
+
+    d.identifier = "filtered_acf";
+    d.name = "Filtered Autocorrelation";
+    d.description = "Filtered autocorrelation of onset detection function";
+    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) {
+        fs = calculateFeatures();
+        ++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;
+    fs = calculateFeatures();
+    ++m_n;
+    return fs;
+}
+
+float
+FixedTempoEstimator::lag2tempo(int lag) {
+    return 60.f / ((lag * m_stepSize) / m_inputSampleRate);
+}
+
+FixedTempoEstimator::FeatureSet
+FixedTempoEstimator::calculateFeatures()
+{
+    FeatureSet fs;
+    Feature feature;
+    feature.hasTimestamp = true;
+    feature.hasDuration = false;
+    feature.label = "";
+    feature.values.clear();
+    feature.values.push_back(0.f);
+
+    char buffer[20];
+    
+    if (m_n < m_dfsize / 4) return fs; // not enough data (perhaps we should return the duration of the input as the "estimated" beat length?)
+
+    std::cerr << "FixedTempoEstimator::calculateTempo: m_n = " << m_n << std::endl;
+    
+    int n = m_n;
+    float *f = m_df;
+
+    for (int i = 0; i < n; ++i) {
+        feature.timestamp = RealTime::frame2RealTime(i * m_stepSize,
+                                                     m_inputSampleRate);
+        std::cerr << "step = " << m_stepSize << ", timestamp = " << feature.timestamp << std::endl;
+        feature.values[0] = f[i];
+        feature.label = "";
+        fs[1].push_back(feature);
+    }
+
+    float *r = new float[n/2];
+    for (int i = 0; i < n/2; ++i) r[i] = 0.f;
+
+    int minlag = 10;
+
+    for (int i = 0; i < n/2; ++i) {
+        for (int j = i; j < n-1; ++j) {
+            r[i] += f[j] * f[j - i];
+        }
+        r[i] /= n - i - 1;
+    }
+
+    for (int i = 0; i < n/2; ++i) {
+        feature.timestamp = RealTime::frame2RealTime(i * m_stepSize,
+                                                     m_inputSampleRate);
+        feature.values[0] = r[i];
+        sprintf(buffer, "%f bpm", lag2tempo(i));
+        feature.label = buffer;
+        fs[2].push_back(feature);
+    }
+
+    float max = 0.f;
+    int maxindex = 0;
+
+    std::cerr << "n/2 = " << n/2 << std::endl;
+
+    for (int i = minlag; i < n/2; ++i) {
+        
+        if (i == minlag || r[i] > max) {
+            max = r[i];
+            maxindex = i;
+        }
+
+        if (i == 0 || i == n/2-1) continue;
+
+        if (r[i] > r[i-1] && r[i] > r[i+1]) {
+            std::cerr << "peak at " << i << " (value=" << r[i] << ", tempo would be " << lag2tempo(i) << ")" << std::endl;
+        }
+    }
+
+    std::cerr << "overall max at " << maxindex << " (value=" << max << ")" << std::endl;
+    
+    float tempo = lag2tempo(maxindex);
+
+    std::cerr << "provisional tempo = " << tempo << std::endl;
+
+    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;
+//    float *filtered = new float[pc];
+//    for (int i = 0; i < pc; ++i) filtered[i] = 0.f;
+
+    int maxpi = 0;
+    float maxp = 0.f;
+
+    for (int i = p0; i <= p1; ++i) {
+
+//        int fi = i - p0;
+
+        float filtered = 0.f;
+        
+        for (int j = 1; j <= (n/2)/p1; ++j) {
+            std::cerr << "j = " << j << ", i = " << i << std::endl;
+            filtered += r[i * j];
+        }
+
+        if (i == p0 || filtered > maxp) {
+            maxp = filtered;
+            maxpi = i;
+        }
+
+        feature.timestamp = RealTime::frame2RealTime(i * m_stepSize,
+                                                     m_inputSampleRate);
+        feature.values[0] = filtered;
+        sprintf(buffer, "%f bpm", lag2tempo(i));
+        feature.label = buffer;
+        fs[3].push_back(feature);
+    }
+
+    std::cerr << "maxpi = " << maxpi << " for tempo " << lag2tempo(maxpi) << " (value = " << maxp << ")" << std::endl;
+    
+    tempo = lag2tempo(maxpi);
+
+    delete[] r;
+
+    feature.hasTimestamp = true;
+    feature.timestamp = m_start;
+    
+    feature.hasDuration = true;
+    feature.duration = m_lasttime - m_start;
+
+    feature.values[0] = tempo;
+
+    fs[0].push_back(feature);
+
+    return fs;
+}