Chris@103: /* -*- c-basic-offset: 4 indent-tabs-mode: nil -*-  vi:set ts=8 sts=4 sw=4: */
Chris@103: 
Chris@103: /*
Chris@103:     Vamp feature extraction plugin using the MATCH audio alignment
Chris@103:     algorithm.
Chris@103: 
Chris@103:     Centre for Digital Music, Queen Mary, University of London.
Chris@236:     Copyright (c) 2007-2020 Simon Dixon, Chris Cannam, and Queen Mary
Chris@230:     University of London, Copyright (c) 2014-2015 Tido GmbH.
Chris@103:     
Chris@103:     This program is free software; you can redistribute it and/or
Chris@103:     modify it under the terms of the GNU General Public License as
Chris@103:     published by the Free Software Foundation; either version 2 of the
Chris@103:     License, or (at your option) any later version.  See the file
Chris@103:     COPYING included with this distribution for more information.
Chris@103: */
Chris@103: 
Chris@103: #include "FeatureConditioner.h"
Chris@103: 
Chris@103: #include <iostream>
Chris@130: #include <cmath>
Chris@103: 
Chris@103: using namespace std;
Chris@103: 
Chris@140: //#define DEBUG_FEATURE_CONDITIONER 1
Chris@140: 
Chris@140: FeatureConditioner::FeatureConditioner(Parameters parameters) :
Chris@140:     m_params(parameters),
Chris@140:     m_ltAverage(0.0)
Chris@140: {
Chris@140: #ifdef DEBUG_FEATURE_CONDITIONER
Chris@140:     cerr << "*** FeatureConditioner: norm = " << parameters.norm
Chris@140:          << ", order = " << parameters.order
Chris@140:          << ", silenceThreshold = " << parameters.silenceThreshold
Chris@140:          << ", decay = " << parameters.decay << endl;
Chris@140: #endif
Chris@140: }
Chris@140: 
Chris@185: feature_t
Chris@185: FeatureConditioner::process(const feature_t &feature)
Chris@103: {
Chris@103:     if (m_prev.empty()) {
Chris@103: 	m_prev.resize(feature.size(), 0.0);
Chris@103:     }
Chris@103:     if (m_prev.size() != feature.size()) {
Chris@103: 	cerr << "ERROR: FeatureConditioner::process: feature size "
Chris@103: 	     << feature.size() << " differs from previous feature size "
Chris@103: 	     << m_prev.size() << endl;
Chris@103: 	return feature;
Chris@103:     }
Chris@103: 
Chris@180:     int size = static_cast<int>(feature.size());
Chris@103:     
Chris@185:     feature_t out(size, 0.0);
Chris@103: 
Chris@103:     double totalEnergy = 0;
Chris@116: 
Chris@116:     switch (m_params.order) {
Chris@116: 
Chris@116:     case OutputRectifiedDerivative:
Chris@103:         for (int i = 0; i < size; i++) {
Chris@103:             totalEnergy += feature[i];
Chris@103:             if (feature[i] > m_prev[i]) {
Chris@103:                 out[i] = feature[i] - m_prev[i];
Chris@103:             } else {
Chris@103:                 out[i] = 0;
Chris@103:             }
Chris@103:         }
Chris@116:         break;
Chris@116: 
Chris@116:     case OutputDerivative:
Chris@103:         for (int i = 0; i < size; i++) {
Chris@116:             totalEnergy += feature[i];
Chris@130:             out[i] = fabs(feature[i] - m_prev[i]);
Chris@116:         }
Chris@116:         break;
Chris@116:         
Chris@116:     case OutputFeatures:
Chris@116:         for (int i = 0; i < size; i++) {
Chris@116:             totalEnergy += feature[i];
Chris@103:             out[i] = feature[i];
Chris@103:         }
Chris@116:         break;
Chris@103:     }
Chris@103: 
Chris@139:     if (m_ltAverage == 0.0) {
Chris@103: 	m_ltAverage = totalEnergy;
Chris@103:     } else {
Chris@103: 	double decay = m_params.decay;
Chris@103:         m_ltAverage = m_ltAverage * decay + totalEnergy * (1.0 - decay);
Chris@103:     }
Chris@103: 
Chris@103:     if (totalEnergy <= m_params.silenceThreshold) {
Chris@103:         for (int i = 0; i < size; i++) {
Chris@103:             out[i] = 0;
Chris@103: 	}
Chris@103:     } else if (m_params.norm == NormaliseToSum1) {
Chris@103:         for (int i = 0; i < size; i++) { 
Chris@185:             out[i] = featurebin_t(out[i] / totalEnergy);
Chris@103: 	}
Chris@103:     } else if (m_params.norm == NormaliseToLTAverage) {
Chris@103:         for (int i = 0; i < size; i++) {
Chris@185:             out[i] = featurebin_t(out[i] / m_ltAverage);
Chris@103: 	}
Chris@103:     }
Chris@103: 
Chris@103:     m_prev = feature;
Chris@103:     return out;
Chris@103: }
Chris@103: