livetrax/libs/qm-dsp/dsp/tonal/ChangeDetectionFunction.cpp

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

/*
    QM DSP Library

    Centre for Digital Music, Queen Mary, University of London.
    This file copyright 2006 Martin Gasser.

    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 "ChangeDetectionFunction.h"

#ifndef PI
#define PI (3.14159265358979232846)
#endif



ChangeDetectionFunction::ChangeDetectionFunction(ChangeDFConfig config) :
	m_dFilterSigma(0.0), m_iFilterWidth(0)
{
	setFilterWidth(config.smoothingWidth);
}

ChangeDetectionFunction::~ChangeDetectionFunction()
{
}

void ChangeDetectionFunction::setFilterWidth(const int iWidth)
{
	m_iFilterWidth = iWidth*2+1;
	
	// it is assumed that the gaussian is 0 outside of +/- FWHM
	// => filter width = 2*FWHM = 2*2.3548*sigma
	m_dFilterSigma = double(m_iFilterWidth) / double(2*2.3548);
	m_vaGaussian.resize(m_iFilterWidth);
	
	double dScale = 1.0 / (m_dFilterSigma*sqrt(2*PI));
	
	for (int x = -(m_iFilterWidth-1)/2; x <= (m_iFilterWidth-1)/2; x++)
	{
		double w = dScale * std::exp ( -(x*x)/(2*m_dFilterSigma*m_dFilterSigma) );
		m_vaGaussian[x + (m_iFilterWidth-1)/2] = w;
	}
	
#ifdef DEBUG_CHANGE_DETECTION_FUNCTION
	std::cerr << "Filter sigma: " << m_dFilterSigma << std::endl;
	std::cerr << "Filter width: " << m_iFilterWidth << std::endl;
#endif
}


ChangeDistance ChangeDetectionFunction::process(const TCSGram& rTCSGram)
{
	ChangeDistance retVal;
	retVal.resize(rTCSGram.getSize(), 0.0);
	
	TCSGram smoothedTCSGram;

	for (int iPosition = 0; iPosition < rTCSGram.getSize(); iPosition++)
	{
		int iSkipLower = 0;
	
		int iLowerPos = iPosition - (m_iFilterWidth-1)/2;
		int iUpperPos = iPosition + (m_iFilterWidth-1)/2;
	
		if (iLowerPos < 0)
		{
			iSkipLower = -iLowerPos;
			iLowerPos = 0;
		}
	
		if (iUpperPos >= rTCSGram.getSize())
		{
			int iMaxIndex = rTCSGram.getSize() - 1;
			iUpperPos = iMaxIndex;
		}
	
		TCSVector smoothedVector;

		// for every bin of the vector, calculate the smoothed value
		for (int iPC = 0; iPC < 6; iPC++)
		{	
			size_t j = 0;
			double dSmoothedValue = 0.0;
			TCSVector rCV;
		
			for (int i = iLowerPos; i <= iUpperPos; i++)
			{
				rTCSGram.getTCSVector(i, rCV);
				dSmoothedValue += m_vaGaussian[iSkipLower + j++] * rCV[iPC];
			}

			smoothedVector[iPC] = dSmoothedValue;
		}
		
		smoothedTCSGram.addTCSVector(smoothedVector);
	}

	for (int iPosition = 0; iPosition < rTCSGram.getSize(); iPosition++)
	{
		/*
			TODO: calculate a confidence measure for the current estimation
			if the current estimate is not confident enough, look further into the future/the past
			e.g., High frequency content, zero crossing rate, spectral flatness
		*/
		
		TCSVector nextTCS;
		TCSVector previousTCS;
		
		int iWindow = 1;

		// while (previousTCS.magnitude() < 0.1 && (iPosition-iWindow) > 0)
		{
			smoothedTCSGram.getTCSVector(iPosition-iWindow, previousTCS);
			// std::cout << previousTCS.magnitude() << std::endl;
			iWindow++;
		}
		
		iWindow = 1;
		
		// while (nextTCS.magnitude() < 0.1 && (iPosition+iWindow) < (rTCSGram.getSize()-1) )
		{
			smoothedTCSGram.getTCSVector(iPosition+iWindow, nextTCS);
			iWindow++;
		}

		double distance = 0.0;
		// Euclidean distance
		for (size_t j = 0; j < 6; j++)
		{
			distance += std::pow(nextTCS[j] - previousTCS[j], 2.0);
		}
	
		retVal[iPosition] = std::pow(distance, 0.5);
	}

	return retVal;
}