view data/model/FFTModel.cpp @ 631:3a5ee4b6c9ad

* Complete the overhaul of CSV file import; now you can pick the purpose for
  each column in the file, and SV should do the rest.  The most significant
  practical improvement here is that we can now handle files in which time
  and duration do not necessarily appear in known columns.

/* -*- 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,
                   size_t windowSize,
                   size_t windowIncrement,
                   size_t fftSize,
                   bool polar,
                   StorageAdviser::Criteria criteria,
                   size_t 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()

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

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

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

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

void
FFTModel::sourceModelAboutToBeDeleted()
{
    if (m_sourceModel) {
        std::cerr << "FFTModel[" << this << "]::sourceModelAboutToBeDeleted(" << m_sourceModel << ")" << std::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,
                    size_t windowSize,
                    size_t windowIncrement,
                    size_t fftSize,
                    bool polar,
                    StorageAdviser::Criteria criteria,
                    size_t 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);
}

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

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

    Column result;

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

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

    return result;
}

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

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

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

    size_t 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);

    size_t 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, size_t x, size_t ymin, size_t 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 (size_t 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).

    size_t sampleRate = getSampleRate();

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

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

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

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

    size_t prevcentre = 0;

    for (size_t 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 (window.size() > medianWinSize) {
            window.pop_front();
        }

        size_t 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)];

        size_t 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()) {
                    size_t peakbin = 0;
                    float peakval = 0.f;
                    for (size_t i = 0; i < 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;
}

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

    size_t fftSize = m_server->getFFTSize() >> m_yshift;
    float binfreq = (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, size_t x,
                             size_t ymin, size_t ymax)
{
    Profiler profiler("FFTModel::getPeakFrequencies");

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

    size_t sampleRate = getSampleRate();
    size_t fftSize = m_server->getFFTSize() >> m_yshift;
    size_t 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));
    }

    size_t 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);
}