view data/model/FFTModel.cpp @ 1008:d9e0e59a1581

When using an aggregate model to pass data to a transform, zero-pad the shorter input to the duration of the longer rather than truncating the longer. (This is better behaviour for e.g. MATCH, and in any case the code was previously truncating incorrectly and ending up with garbage data at the end.)

/* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */

/*
    Sonic Visualiser
    An audio file viewer and annotation editor.
    Centre for Digital Music, Queen Mary, University of London.
    This file copyright 2006 Chris Cannam.
    
    This program is free software; you can redistribute it and/or
    modify it under the terms of the GNU General Public License as
    published by the Free Software Foundation; either version 2 of the
    License, or (at your option) any later version.  See the file
    COPYING included with this distribution for more information.
*/

#include "FFTModel.h"
#include "DenseTimeValueModel.h"
#include "AggregateWaveModel.h"

#include "base/Profiler.h"
#include "base/Pitch.h"

#include <algorithm>

#include <cassert>

#ifndef __GNUC__
#include <alloca.h>
#endif

FFTModel::FFTModel(const DenseTimeValueModel *model,
                   int channel,
                   WindowType windowType,
                   int windowSize,
                   int windowIncrement,
                   int fftSize,
                   bool polar,
                   StorageAdviser::Criteria criteria,
                   int fillFromColumn) :
    //!!! ZoomConstraint!
    m_server(0),
    m_xshift(0),
    m_yshift(0)
{
    setSourceModel(const_cast<DenseTimeValueModel *>(model)); //!!! hmm.

    m_server = getServer(model,
                         channel,
                         windowType,
                         windowSize,
                         windowIncrement,
                         fftSize,
                         polar,
                         criteria,
                         fillFromColumn);

    if (!m_server) return; // caller should check isOK()

    int xratio = windowIncrement / m_server->getWindowIncrement();
    int yratio = m_server->getFFTSize() / fftSize;

    while (xratio > 1) {
        if (xratio & 0x1) {
            cerr << "ERROR: FFTModel: Window increment ratio "
                      << windowIncrement << " / "
                      << m_server->getWindowIncrement()
                      << " must be a power of two" << endl;
            assert(!(xratio & 0x1));
        }
        ++m_xshift;
        xratio >>= 1;
    }

    while (yratio > 1) {
        if (yratio & 0x1) {
            cerr << "ERROR: FFTModel: FFT size ratio "
                      << m_server->getFFTSize() << " / " << fftSize
                      << " must be a power of two" << endl;
            assert(!(yratio & 0x1));
        }
        ++m_yshift;
        yratio >>= 1;
    }
}

FFTModel::~FFTModel()
{
    if (m_server) FFTDataServer::releaseInstance(m_server);
}

void
FFTModel::sourceModelAboutToBeDeleted()
{
    if (m_sourceModel) {
        cerr << "FFTModel[" << this << "]::sourceModelAboutToBeDeleted(" << m_sourceModel << ")" << endl;
        if (m_server) {
            FFTDataServer::releaseInstance(m_server);
            m_server = 0;
        }
        FFTDataServer::modelAboutToBeDeleted(m_sourceModel);
    }
}

FFTDataServer *
FFTModel::getServer(const DenseTimeValueModel *model,
                    int channel,
                    WindowType windowType,
                    int windowSize,
                    int windowIncrement,
                    int fftSize,
                    bool polar,
                    StorageAdviser::Criteria criteria,
                    int fillFromColumn)
{
    // Obviously, an FFT model of channel C (where C != -1) of an
    // aggregate model is the same as the FFT model of the appropriate
    // channel of whichever model that aggregate channel is drawn
    // from.  We should use that model here, in case we already have
    // the data for it or will be wanting the same data again later.

    // If the channel is -1 (i.e. mixture of all channels), then we
    // can't do this shortcut unless the aggregate model only has one
    // channel or contains exactly all of the channels of a single
    // other model.  That isn't very likely -- if it were the case,
    // why would we be using an aggregate model?

    if (channel >= 0) {

        const AggregateWaveModel *aggregate =
            dynamic_cast<const AggregateWaveModel *>(model);

        if (aggregate && channel < aggregate->getComponentCount()) {

            AggregateWaveModel::ModelChannelSpec spec =
                aggregate->getComponent(channel);

            return getServer(spec.model,
                             spec.channel,
                             windowType,
                             windowSize,
                             windowIncrement,
                             fftSize,
                             polar,
                             criteria,
                             fillFromColumn);
        }
    }

    // The normal case

    return FFTDataServer::getFuzzyInstance(model,
                                           channel,
                                           windowType,
                                           windowSize,
                                           windowIncrement,
                                           fftSize,
                                           polar,
                                           criteria,
                                           fillFromColumn);
}

int
FFTModel::getSampleRate() const
{
    return isOK() ? m_server->getModel()->getSampleRate() : 0;
}

FFTModel::Column
FFTModel::getColumn(int x) const
{
    Profiler profiler("FFTModel::getColumn", false);

    Column result;

    result.clear();
    int h = getHeight();
    result.reserve(h);

#ifdef __GNUC__
    float magnitudes[h];
#else
    float *magnitudes = (float *)alloca(h * sizeof(float));
#endif

    if (m_server->getMagnitudesAt(x << m_xshift, magnitudes)) {

        for (int y = 0; y < h; ++y) {
            result.push_back(magnitudes[y]);
        }

    } else {
        for (int i = 0; i < h; ++i) result.push_back(0.f);
    }

    return result;
}

QString
FFTModel::getBinName(int n) const
{
    int sr = getSampleRate();
    if (!sr) return "";
    QString name = tr("%1 Hz").arg((n * sr) / ((getHeight()-1) * 2));
    return name;
}

bool
FFTModel::estimateStableFrequency(int x, int y, float &frequency)
{
    if (!isOK()) return false;

    int sampleRate = m_server->getModel()->getSampleRate();

    int fftSize = m_server->getFFTSize() >> m_yshift;
    frequency = (float(y) * sampleRate) / fftSize;

    if (x+1 >= getWidth()) return false;

    // At frequency f, a phase shift of 2pi (one cycle) happens in 1/f sec.
    // At hopsize h and sample rate sr, one hop happens in h/sr sec.
    // At window size w, for bin b, f is b*sr/w.
    // thus 2pi phase shift happens in w/(b*sr) sec.
    // We need to know what phase shift we expect from h/sr sec.
    // -> 2pi * ((h/sr) / (w/(b*sr)))
    //  = 2pi * ((h * b * sr) / (w * sr))
    //  = 2pi * (h * b) / w.

    float oldPhase = getPhaseAt(x, y);
    float newPhase = getPhaseAt(x+1, y);

    int incr = getResolution();

    float expectedPhase = oldPhase + (2.0 * M_PI * y * incr) / fftSize;

    float phaseError = princargf(newPhase - expectedPhase);

//    bool stable = (fabsf(phaseError) < (1.1f * (m_windowIncrement * M_PI) / m_fftSize));

    // The new frequency estimate based on the phase error resulting
    // from assuming the "native" frequency of this bin

    frequency =
        (sampleRate * (expectedPhase + phaseError - oldPhase)) /
        (2 * M_PI * incr);

    return true;
}

FFTModel::PeakLocationSet
FFTModel::getPeaks(PeakPickType type, int x, int ymin, int ymax)
{
    Profiler profiler("FFTModel::getPeaks");

    FFTModel::PeakLocationSet peaks;
    if (!isOK()) return peaks;

    if (ymax == 0 || ymax > getHeight() - 1) {
        ymax = getHeight() - 1;
    }

    if (type == AllPeaks) {
        int minbin = ymin;
        if (minbin > 0) minbin = minbin - 1;
        int maxbin = ymax;
        if (maxbin < getHeight() - 1) maxbin = maxbin + 1;
        const int n = maxbin - minbin + 1;
#ifdef __GNUC__
        float values[n];
#else
        float *values = (float *)alloca(n * sizeof(float));
#endif
        getMagnitudesAt(x, values, minbin, maxbin - minbin + 1);
        for (int bin = ymin; bin <= ymax; ++bin) {
            if (bin == minbin || bin == maxbin) continue;
            if (values[bin - minbin] > values[bin - minbin - 1] &&
                values[bin - minbin] > values[bin - minbin + 1]) {
                peaks.insert(bin);
            }
        }
        return peaks;
    }

    Column values = getColumn(x);

    float mean = 0.f;
    for (int i = 0; i < values.size(); ++i) mean += values[i];
    if (values.size() >0) mean /= values.size();

    // For peak picking we use a moving median window, picking the
    // highest value within each continuous region of values that
    // exceed the median.  For pitch adaptivity, we adjust the window
    // size to a roughly constant pitch range (about four tones).

    int sampleRate = getSampleRate();

    std::deque<float> window;
    std::vector<int> inrange;
    float dist = 0.5;

    int medianWinSize = getPeakPickWindowSize(type, sampleRate, ymin, dist);
    int halfWin = medianWinSize/2;

    int binmin;
    if (ymin > halfWin) binmin = ymin - halfWin;
    else binmin = 0;

    int binmax;
    if (ymax + halfWin < values.size()) binmax = ymax + halfWin;
    else binmax = values.size()-1;

    int prevcentre = 0;

    for (int bin = binmin; bin <= binmax; ++bin) {

        float value = values[bin];

        window.push_back(value);

        // so-called median will actually be the dist*100'th percentile
        medianWinSize = getPeakPickWindowSize(type, sampleRate, bin, dist);
        halfWin = medianWinSize/2;

        while ((int)window.size() > medianWinSize) {
            window.pop_front();
        }

        int actualSize = window.size();

        if (type == MajorPitchAdaptivePeaks) {
            if (ymax + halfWin < values.size()) binmax = ymax + halfWin;
            else binmax = values.size()-1;
        }

        std::deque<float> sorted(window);
        std::sort(sorted.begin(), sorted.end());
        float median = sorted[int(sorted.size() * dist)];

        int centrebin = 0;
        if (bin > actualSize/2) centrebin = bin - actualSize/2;
        
        while (centrebin > prevcentre || bin == binmin) {

            if (centrebin > prevcentre) ++prevcentre;

            float centre = values[prevcentre];

            if (centre > median) {
                inrange.push_back(centrebin);
            }

            if (centre <= median || centrebin+1 == values.size()) {
                if (!inrange.empty()) {
                    int peakbin = 0;
                    float peakval = 0.f;
                    for (int i = 0; i < (int)inrange.size(); ++i) {
                        if (i == 0 || values[inrange[i]] > peakval) {
                            peakval = values[inrange[i]];
                            peakbin = inrange[i];
                        }
                    }
                    inrange.clear();
                    if (peakbin >= ymin && peakbin <= ymax) {
                        peaks.insert(peakbin);
                    }
                }
            }

            if (bin == binmin) break;
        }
    }

    return peaks;
}

int
FFTModel::getPeakPickWindowSize(PeakPickType type, int sampleRate,
                                int bin, float &percentile) const
{
    percentile = 0.5;
    if (type == MajorPeaks) return 10;
    if (bin == 0) return 3;

    int fftSize = m_server->getFFTSize() >> m_yshift;
    float binfreq = (float(sampleRate) * bin) / fftSize;
    float hifreq = Pitch::getFrequencyForPitch(73, 0, binfreq);

    int hibin = lrintf((hifreq * fftSize) / sampleRate);
    int medianWinSize = hibin - bin;
    if (medianWinSize < 3) medianWinSize = 3;

    percentile = 0.5 + (binfreq / sampleRate);

    return medianWinSize;
}

FFTModel::PeakSet
FFTModel::getPeakFrequencies(PeakPickType type, int x,
                             int ymin, int ymax)
{
    Profiler profiler("FFTModel::getPeakFrequencies");

    PeakSet peaks;
    if (!isOK()) return peaks;
    PeakLocationSet locations = getPeaks(type, x, ymin, ymax);

    int sampleRate = getSampleRate();
    int fftSize = m_server->getFFTSize() >> m_yshift;
    int incr = getResolution();

    // This duplicates some of the work of estimateStableFrequency to
    // allow us to retrieve the phases in two separate vertical
    // columns, instead of jumping back and forth between columns x and
    // x+1, which may be significantly slower if re-seeking is needed

    std::vector<float> phases;
    for (PeakLocationSet::iterator i = locations.begin();
         i != locations.end(); ++i) {
        phases.push_back(getPhaseAt(x, *i));
    }

    int phaseIndex = 0;
    for (PeakLocationSet::iterator i = locations.begin();
         i != locations.end(); ++i) {
        float oldPhase = phases[phaseIndex];
        float newPhase = getPhaseAt(x+1, *i);
        float expectedPhase = oldPhase + (2.0 * M_PI * *i * incr) / fftSize;
        float phaseError = princargf(newPhase - expectedPhase);
        float frequency =
            (sampleRate * (expectedPhase + phaseError - oldPhase))
            / (2 * M_PI * incr);
//        bool stable = (fabsf(phaseError) < (1.1f * (incr * M_PI) / fftSize));
//        if (stable)
        peaks[*i] = frequency;
        ++phaseIndex;
    }

    return peaks;
}

Model *
FFTModel::clone() const
{
    return new FFTModel(*this);
}

FFTModel::FFTModel(const FFTModel &model) :
    DenseThreeDimensionalModel(),
    m_server(model.m_server),
    m_xshift(model.m_xshift),
    m_yshift(model.m_yshift)
{
    FFTDataServer::claimInstance(m_server);
}