diff plugins/OnsetDetect.cpp @ 27:3256bfa04ed8

* split out tempo/beat/onset plugin into tempo/beat and onset

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/plugins/OnsetDetect.cpp	Mon May 21 13:09:12 2007 +0000
@@ -0,0 +1,404 @@
+/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */
+
+/*
+    QM Vamp Plugin Set
+
+    Centre for Digital Music, Queen Mary, University of London.
+    All rights reserved.
+*/
+
+#include "OnsetDetect.h"
+
+#include <dsp/onsets/DetectionFunction.h>
+#include <dsp/onsets/PeakPicking.h>
+#include <dsp/tempotracking/TempoTrack.h>
+
+using std::string;
+using std::vector;
+using std::cerr;
+using std::endl;
+
+float OnsetDetector::m_stepSecs = 0.01161;
+
+class OnsetDetectorData
+{
+public:
+    OnsetDetectorData(const DFConfig &config) : dfConfig(config) {
+	df = new DetectionFunction(config);
+    }
+    ~OnsetDetectorData() {
+	delete df;
+    }
+    void reset() {
+	delete df;
+	df = new DetectionFunction(dfConfig);
+	dfOutput.clear();
+    }
+
+    DFConfig dfConfig;
+    DetectionFunction *df;
+    vector<double> dfOutput;
+};
+    
+
+OnsetDetector::OnsetDetector(float inputSampleRate) :
+    Vamp::Plugin(inputSampleRate),
+    m_d(0),
+    m_dfType(DF_COMPLEXSD),
+    m_sensitivity(50)
+{
+}
+
+OnsetDetector::~OnsetDetector()
+{
+    delete m_d;
+}
+
+string
+OnsetDetector::getIdentifier() const
+{
+    return "qm-onsetdetector";
+}
+
+string
+OnsetDetector::getName() const
+{
+    return "Note Onset Detector";
+}
+
+string
+OnsetDetector::getDescription() const
+{
+    return "Estimate individual note onset positions";
+}
+
+string
+OnsetDetector::getMaker() const
+{
+    return "Christian Landone, Chris Duxbury and Juan Pablo Bello, Queen Mary, University of London";
+}
+
+int
+OnsetDetector::getPluginVersion() const
+{
+    return 1;
+}
+
+string
+OnsetDetector::getCopyright() const
+{
+    return "Copyright (c) 2006-2007 - All Rights Reserved";
+}
+
+OnsetDetector::ParameterList
+OnsetDetector::getParameterDescriptors() const
+{
+    ParameterList list;
+
+    ParameterDescriptor desc;
+    desc.identifier = "dftype";
+    desc.name = "Onset Detection Function Type";
+    desc.description = "Method used to calculate the onset detection function";
+    desc.minValue = 0;
+    desc.maxValue = 3;
+    desc.defaultValue = 3;
+    desc.isQuantized = true;
+    desc.quantizeStep = 1;
+    desc.valueNames.push_back("High-Frequency Content");
+    desc.valueNames.push_back("Spectral Difference");
+    desc.valueNames.push_back("Phase Deviation");
+    desc.valueNames.push_back("Complex Domain");
+    desc.valueNames.push_back("Broadband Energy Rise");
+    list.push_back(desc);
+
+    desc.identifier = "sensitivity";
+    desc.name = "Onset Detector Sensitivity";
+    desc.description = "Sensitivity of peak-picker for onset detection";
+    desc.minValue = 0;
+    desc.maxValue = 100;
+    desc.defaultValue = 50;
+    desc.isQuantized = true;
+    desc.quantizeStep = 1;
+    desc.unit = "%";
+    desc.valueNames.clear();
+    list.push_back(desc);
+
+    return list;
+}
+
+float
+OnsetDetector::getParameter(std::string name) const
+{
+    if (name == "dftype") {
+        switch (m_dfType) {
+        case DF_HFC: return 0;
+        case DF_SPECDIFF: return 1;
+        case DF_PHASEDEV: return 2;
+        default: case DF_COMPLEXSD: return 3;
+        case DF_BROADBAND: return 4;
+        }
+    } else if (name == "sensitivity") {
+        return m_sensitivity;
+    }
+    return 0.0;
+}
+
+void
+OnsetDetector::setParameter(std::string name, float value)
+{
+    if (name == "dftype") {
+        switch (lrintf(value)) {
+        case 0: m_dfType = DF_HFC; break;
+        case 1: m_dfType = DF_SPECDIFF; break;
+        case 2: m_dfType = DF_PHASEDEV; break;
+        default: case 3: m_dfType = DF_COMPLEXSD; break;
+        case 4: m_dfType = DF_BROADBAND; break;
+        }
+    } else if (name == "sensitivity") {
+        m_sensitivity = value;
+    }
+}
+
+bool
+OnsetDetector::initialise(size_t channels, size_t stepSize, size_t blockSize)
+{
+    if (m_d) {
+	delete m_d;
+	m_d = 0;
+    }
+
+    if (channels < getMinChannelCount() ||
+	channels > getMaxChannelCount()) {
+        std::cerr << "OnsetDetector::initialise: Unsupported channel count: "
+                  << channels << std::endl;
+        return false;
+    }
+
+    if (blockSize != getPreferredStepSize() * 2) {
+        std::cerr << "OnsetDetector::initialise: Unsupported block size for this sample rate: "
+                  << blockSize << " (wanted " << (getPreferredStepSize() * 2) << ")" << std::endl;
+        return false;
+    }
+
+    if (stepSize != getPreferredStepSize()) {
+        std::cerr << "OnsetDetector::initialise: Unsupported step size for this sample rate: "
+                  << stepSize << " (wanted " << (getPreferredStepSize()) << ")" << std::endl;
+        return false;
+    }
+
+    DFConfig dfConfig;
+    dfConfig.DFType = m_dfType;
+    dfConfig.stepSecs = float(stepSize) / m_inputSampleRate;
+    dfConfig.stepSize = stepSize;
+    dfConfig.frameLength = blockSize;
+    dfConfig.dbRise = 6.0 - m_sensitivity / 16.6667;
+    
+    m_d = new OnsetDetectorData(dfConfig);
+    return true;
+}
+
+void
+OnsetDetector::reset()
+{
+    if (m_d) m_d->reset();
+}
+
+size_t
+OnsetDetector::getPreferredStepSize() const
+{
+    size_t step = size_t(m_inputSampleRate * m_stepSecs + 0.0001);
+//    std::cerr << "OnsetDetector::getPreferredStepSize: input sample rate is " << m_inputSampleRate << ", step size is " << step << std::endl;
+    return step;
+}
+
+size_t
+OnsetDetector::getPreferredBlockSize() const
+{
+    return getPreferredStepSize() * 2;
+}
+
+OnsetDetector::OutputList
+OnsetDetector::getOutputDescriptors() const
+{
+    OutputList list;
+
+    OutputDescriptor onsets;
+    onsets.identifier = "onsets";
+    onsets.name = "Note Onsets";
+    onsets.description = "Perceived note onset positions";
+    onsets.unit = "";
+    onsets.hasFixedBinCount = true;
+    onsets.binCount = 0;
+    onsets.sampleType = OutputDescriptor::VariableSampleRate;
+    onsets.sampleRate = 1.0 / m_stepSecs;
+
+    OutputDescriptor df;
+    df.identifier = "detection_fn";
+    df.name = "Onset Detection Function";
+    df.description = "Probability function of note onset likelihood";
+    df.unit = "";
+    df.hasFixedBinCount = true;
+    df.binCount = 1;
+    df.hasKnownExtents = false;
+    df.isQuantized = false;
+    df.sampleType = OutputDescriptor::OneSamplePerStep;
+
+    OutputDescriptor sdf;
+    sdf.identifier = "smoothed_df";
+    sdf.name = "Smoothed Detection Function";
+    sdf.description = "Smoothed probability function used for peak-picking";
+    sdf.unit = "";
+    sdf.hasFixedBinCount = true;
+    sdf.binCount = 1;
+    sdf.hasKnownExtents = false;
+    sdf.isQuantized = false;
+
+    sdf.sampleType = OutputDescriptor::VariableSampleRate;
+
+//!!! SV doesn't seem to handle these correctly in getRemainingFeatures
+//    sdf.sampleType = OutputDescriptor::FixedSampleRate;
+    sdf.sampleRate = 1.0 / m_stepSecs;
+
+    list.push_back(onsets);
+    list.push_back(df);
+    list.push_back(sdf);
+
+    return list;
+}
+
+OnsetDetector::FeatureSet
+OnsetDetector::process(const float *const *inputBuffers,
+                      Vamp::RealTime /* timestamp */)
+{
+    if (!m_d) {
+	cerr << "ERROR: OnsetDetector::process: "
+	     << "OnsetDetector has not been initialised"
+	     << endl;
+	return FeatureSet();
+    }
+
+    size_t len = m_d->dfConfig.frameLength / 2;
+
+    double *magnitudes = new double[len];
+    double *phases = new double[len];
+
+    // We only support a single input channel
+
+    for (size_t i = 0; i < len; ++i) {
+
+        magnitudes[i] = sqrt(inputBuffers[0][i*2  ] * inputBuffers[0][i*2  ] +
+                             inputBuffers[0][i*2+1] * inputBuffers[0][i*2+1]);
+
+	phases[i] = atan2(-inputBuffers[0][i*2+1], inputBuffers[0][i*2]);
+    }
+
+    double output = m_d->df->process(magnitudes, phases);
+
+    delete[] magnitudes;
+    delete[] phases;
+
+    m_d->dfOutput.push_back(output);
+
+    FeatureSet returnFeatures;
+
+    Feature feature;
+    feature.hasTimestamp = false;
+    feature.values.push_back(output);
+
+    returnFeatures[1].push_back(feature); // detection function is output 1
+    return returnFeatures;
+}
+
+OnsetDetector::FeatureSet
+OnsetDetector::getRemainingFeatures()
+{
+    if (!m_d) {
+	cerr << "ERROR: OnsetDetector::getRemainingFeatures: "
+	     << "OnsetDetector has not been initialised"
+	     << endl;
+	return FeatureSet();
+    }
+
+    if (m_dfType == DF_BROADBAND) {
+        for (size_t i = 0; i < m_d->dfOutput.size(); ++i) {
+            if (m_d->dfOutput[i] < ((110 - m_sensitivity) *
+                                    m_d->dfConfig.frameLength) / 200) {
+                m_d->dfOutput[i] = 0;
+            }
+        }
+    }
+
+    double aCoeffs[] = { 1.0000, -0.5949, 0.2348 };
+    double bCoeffs[] = { 0.1600,  0.3200, 0.1600 };
+
+    FeatureSet returnFeatures;
+
+    PPickParams ppParams;
+    ppParams.length = m_d->dfOutput.size();
+    // tau and cutoff appear to be unused in PeakPicking, but I've
+    // inserted some moderately plausible values rather than leave
+    // them unset.  The QuadThresh values come from trial and error.
+    // The rest of these are copied from ttParams in the BeatTracker
+    // code: I don't claim to know whether they're good or not --cc
+    ppParams.tau = m_d->dfConfig.stepSize / m_inputSampleRate;
+    ppParams.alpha = 9;
+    ppParams.cutoff = m_inputSampleRate/4;
+    ppParams.LPOrd = 2;
+    ppParams.LPACoeffs = aCoeffs;
+    ppParams.LPBCoeffs = bCoeffs;
+    ppParams.WinT.post = 8;
+    ppParams.WinT.pre = 7;
+    ppParams.QuadThresh.a = (100 - m_sensitivity) / 1000.0;
+    ppParams.QuadThresh.b = 0;
+    ppParams.QuadThresh.c = (100 - m_sensitivity) / 1500.0;
+
+    PeakPicking peakPicker(ppParams);
+
+    double *ppSrc = new double[ppParams.length];
+    for (unsigned int i = 0; i < ppParams.length; ++i) {
+        ppSrc[i] = m_d->dfOutput[i];
+    }
+
+    vector<int> onsets;
+    peakPicker.process(ppSrc, ppParams.length, onsets);
+
+    for (size_t i = 0; i < onsets.size(); ++i) {
+
+        size_t index = onsets[i];
+
+        if (m_dfType != DF_BROADBAND) {
+            double prevDiff = 0.0;
+            while (index > 1) {
+                double diff = ppSrc[index] - ppSrc[index-1];
+                if (diff < prevDiff * 0.9) break;
+                prevDiff = diff;
+                --index;
+            }
+        }
+
+	size_t frame = index * m_d->dfConfig.stepSize;
+
+	Feature feature;
+	feature.hasTimestamp = true;
+	feature.timestamp = Vamp::RealTime::frame2RealTime
+	    (frame, lrintf(m_inputSampleRate));
+
+	returnFeatures[0].push_back(feature); // onsets are output 0
+    }
+
+    for (int i = 0; i < ppParams.length; ++i) {
+        
+        Feature feature;
+//        feature.hasTimestamp = false;
+        feature.hasTimestamp = true;
+	size_t frame = i * m_d->dfConfig.stepSize;
+	feature.timestamp = Vamp::RealTime::frame2RealTime
+	    (frame, lrintf(m_inputSampleRate));
+
+        feature.values.push_back(ppSrc[i]);
+        returnFeatures[2].push_back(feature); // smoothed df is output 2
+    }
+
+    return returnFeatures;
+}
+