view dsp/tonal/ChangeDetectionFunction.cpp @ 321:f1e6be2de9a5

A threshold (delta) is added in the peak picking parameters structure (PPickParams). It is used as an offset when computing the smoothed detection function. A constructor for the structure PPickParams is also added to set the parameters to 0 when a structure instance is created. Hence programmes using the peak picking parameter structure and which do not set the delta parameter (e.g. QM Vamp note onset detector) won't be affected by the modifications.

Functions modified:
- dsp/onsets/PeakPicking.cpp
- dsp/onsets/PeakPicking.h
- dsp/signalconditioning/DFProcess.cpp
- dsp/signalconditioning/DFProcess.h

/* -*- 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;
}