Chris@26: /* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */
Chris@26: 
Chris@26: /*
Chris@26:     Vamp feature extraction plugin using the MATCH audio alignment
Chris@26:     algorithm.
Chris@26: 
Chris@26:     Centre for Digital Music, Queen Mary, University of London.
Chris@26:     This file copyright 2007 Simon Dixon, Chris Cannam and QMUL.
Chris@26:     
Chris@26:     This program is free software; you can redistribute it and/or
Chris@26:     modify it under the terms of the GNU General Public License as
Chris@26:     published by the Free Software Foundation; either version 2 of the
Chris@26:     License, or (at your option) any later version.  See the file
Chris@26:     COPYING included with this distribution for more information.
Chris@26: */
Chris@26: 
Chris@26: #include "DistanceMetric.h"
Chris@26: 
Chris@26: #include <cassert>
Chris@26: #include <cmath>
Chris@133: #include <iostream>
Chris@26: 
Chris@133: using namespace std;
Chris@26: 
Chris@140: //#define DEBUG_DISTANCE_METRIC 1
Chris@140: 
Chris@143: DistanceMetric::DistanceMetric(Parameters params) :
Chris@143:     m_params(params)
Chris@140: {
Chris@140: #ifdef DEBUG_DISTANCE_METRIC
Chris@143:     cerr << "*** DistanceMetric: norm = " << m_params.norm
Chris@143:          << endl;
Chris@140: #endif
Chris@140: }
Chris@140: 
Chris@26: double
Chris@26: DistanceMetric::calcDistance(const vector<double> &f1,
Chris@26: 			     const vector<double> &f2)
Chris@26: {
Chris@26:     double d = 0;
Chris@26:     double sum = 0;
Chris@26: 
Chris@26:     int featureSize = f1.size();
Chris@26:     assert(int(f2.size()) == featureSize);
Chris@145: 
Chris@26:     for (int i = 0; i < featureSize; i++) {
Chris@26:         d += fabs(f1[i] - f2[i]);
Chris@145:         sum += fabs(f1[i]) + fabs(f2[i]);
Chris@26:     }
Chris@26: 
Chris@145:     double noise = 1e-3 * featureSize;
Chris@145:     d += noise;
Chris@145:     sum += noise;
Chris@145:     
Chris@143:     if (sum == 0) {
Chris@26:         return 0;
Chris@143:     }
Chris@26: 
Chris@143:     double distance = 0;
Chris@26: 
Chris@143:     if (m_params.norm == NormaliseDistanceToSum) {
Chris@143: 
Chris@143:         distance = d / sum; // 0 <= d/sum <= 2
Chris@143: 
Chris@143:     } else if (m_params.norm == NormaliseDistanceToLogSum) {
Chris@143: 
Chris@143:         // note if this were to be restored, it would have to use
Chris@143:         // totalEnergies vector instead of f1[freqMapSize] which used to
Chris@143:         // store the total energy:
Chris@143:         //	double weight = (5 + Math.log(f1[freqMapSize] + f2[freqMapSize]))/10.0;
Chris@143: 
Chris@143:         double weight = (8 + log(sum)) / 10.0;
Chris@133:     
Chris@143:         if (weight < 0) weight = 0;
Chris@143:         else if (weight > 1) weight = 1;
Chris@26: 
Chris@143:         distance = d / sum * weight;
Chris@143: 
Chris@143:     } else {
Chris@143: 
Chris@143:         distance = d;
Chris@143:     }
Chris@143:     
Chris@143:     return distance;
Chris@26: }
Chris@26: