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1 /* -*- c-basic-offset: 4 indent-tabs-mode: nil -*- vi:set ts=8 sts=4 sw=4: */
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2
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3 /*
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4 Vamp
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5
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6 An API for audio analysis and feature extraction plugins.
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7
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8 Centre for Digital Music, Queen Mary, University of London.
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9 Copyright 2006-2008 Chris Cannam and QMUL.
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10
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11 Permission is hereby granted, free of charge, to any person
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12 obtaining a copy of this software and associated documentation
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13 files (the "Software"), to deal in the Software without
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14 restriction, including without limitation the rights to use, copy,
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15 modify, merge, publish, distribute, sublicense, and/or sell copies
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16 of the Software, and to permit persons to whom the Software is
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17 furnished to do so, subject to the following conditions:
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18
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19 The above copyright notice and this permission notice shall be
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20 included in all copies or substantial portions of the Software.
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21
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22 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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23 EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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24 MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
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25 NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR
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26 ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF
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27 CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
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28 WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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29
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30 Except as contained in this notice, the names of the Centre for
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31 Digital Music; Queen Mary, University of London; and Chris Cannam
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32 shall not be used in advertising or otherwise to promote the sale,
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33 use or other dealings in this Software without prior written
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34 authorization.
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35 */
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36
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37 #include "FixedTempoEstimator.h"
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38
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39 using std::string;
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40 using std::vector;
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41 using std::cerr;
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42 using std::endl;
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43
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44 using Vamp::RealTime;
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45
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46 #include <cmath>
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47
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48
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49 FixedTempoEstimator::FixedTempoEstimator(float inputSampleRate) :
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50 Plugin(inputSampleRate),
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51 m_stepSize(0),
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52 m_blockSize(0),
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53 m_priorMagnitudes(0),
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54 m_df(0),
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55 m_r(0),
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56 m_fr(0),
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57 m_n(0)
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58 {
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59 }
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60
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61 FixedTempoEstimator::~FixedTempoEstimator()
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62 {
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63 delete[] m_priorMagnitudes;
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64 delete[] m_df;
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65 delete[] m_r;
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66 delete[] m_fr;
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67 }
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68
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69 string
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70 FixedTempoEstimator::getIdentifier() const
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71 {
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72 return "fixedtempo";
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73 }
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74
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75 string
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76 FixedTempoEstimator::getName() const
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77 {
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78 return "Simple Fixed Tempo Estimator";
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79 }
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80
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81 string
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82 FixedTempoEstimator::getDescription() const
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83 {
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84 return "Study a short section of audio and estimate its tempo, assuming the tempo is constant";
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85 }
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86
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87 string
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88 FixedTempoEstimator::getMaker() const
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89 {
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90 return "Vamp SDK Example Plugins";
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91 }
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92
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93 int
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94 FixedTempoEstimator::getPluginVersion() const
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95 {
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96 return 1;
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97 }
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98
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99 string
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100 FixedTempoEstimator::getCopyright() const
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101 {
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102 return "Code copyright 2008 Queen Mary, University of London. Freely redistributable (BSD license)";
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103 }
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104
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105 size_t
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106 FixedTempoEstimator::getPreferredStepSize() const
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107 {
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108 return 0;
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109 }
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110
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111 size_t
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112 FixedTempoEstimator::getPreferredBlockSize() const
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113 {
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114 return 64;
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115 }
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116
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117 bool
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118 FixedTempoEstimator::initialise(size_t channels, size_t stepSize, size_t blockSize)
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119 {
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120 if (channels < getMinChannelCount() ||
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121 channels > getMaxChannelCount()) return false;
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122
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123 m_stepSize = stepSize;
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124 m_blockSize = blockSize;
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125
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126 float dfLengthSecs = 8.f;
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127 m_dfsize = (dfLengthSecs * m_inputSampleRate) / m_stepSize;
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128
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129 m_priorMagnitudes = new float[m_blockSize/2];
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130 m_df = new float[m_dfsize];
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131
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132 for (size_t i = 0; i < m_blockSize/2; ++i) {
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133 m_priorMagnitudes[i] = 0.f;
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134 }
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135 for (size_t i = 0; i < m_dfsize; ++i) {
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136 m_df[i] = 0.f;
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137 }
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138
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139 m_n = 0;
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140
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141 return true;
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142 }
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143
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144 void
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145 FixedTempoEstimator::reset()
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146 {
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147 std::cerr << "FixedTempoEstimator: reset called" << std::endl;
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148
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149 if (!m_priorMagnitudes) return;
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150
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151 std::cerr << "FixedTempoEstimator: resetting" << std::endl;
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152
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153 for (size_t i = 0; i < m_blockSize/2; ++i) {
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154 m_priorMagnitudes[i] = 0.f;
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155 }
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156 for (size_t i = 0; i < m_dfsize; ++i) {
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157 m_df[i] = 0.f;
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158 }
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159
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160 delete[] m_r;
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161 m_r = 0;
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162
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163 delete[] m_fr;
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164 m_fr = 0;
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165
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166 m_n = 0;
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167
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168 m_start = RealTime::zeroTime;
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169 m_lasttime = RealTime::zeroTime;
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170 }
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171
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172 FixedTempoEstimator::ParameterList
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173 FixedTempoEstimator::getParameterDescriptors() const
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174 {
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175 ParameterList list;
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176 return list;
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177 }
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178
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179 float
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180 FixedTempoEstimator::getParameter(std::string id) const
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181 {
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182 return 0.f;
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183 }
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184
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185 void
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186 FixedTempoEstimator::setParameter(std::string id, float value)
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187 {
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188 }
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189
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190 static int TempoOutput = 0;
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191 static int CandidatesOutput = 1;
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192 static int DFOutput = 2;
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193 static int ACFOutput = 3;
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194 static int FilteredACFOutput = 4;
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195
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196 FixedTempoEstimator::OutputList
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197 FixedTempoEstimator::getOutputDescriptors() const
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198 {
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199 OutputList list;
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200
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201 OutputDescriptor d;
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202 d.identifier = "tempo";
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203 d.name = "Tempo";
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204 d.description = "Estimated tempo";
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205 d.unit = "bpm";
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206 d.hasFixedBinCount = true;
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207 d.binCount = 1;
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208 d.hasKnownExtents = false;
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209 d.isQuantized = false;
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210 d.sampleType = OutputDescriptor::VariableSampleRate;
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211 d.sampleRate = m_inputSampleRate;
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212 d.hasDuration = true; // our returned tempo spans a certain range
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213 list.push_back(d);
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214
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215 d.identifier = "candidates";
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216 d.name = "Tempo candidates";
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217 d.description = "Possible tempo estimates, one per bin with the most likely in the first bin";
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218 d.unit = "bpm";
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219 d.hasFixedBinCount = false;
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220 list.push_back(d);
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221
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222 d.identifier = "detectionfunction";
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223 d.name = "Detection Function";
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224 d.description = "Onset detection function";
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225 d.unit = "";
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226 d.hasFixedBinCount = 1;
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227 d.binCount = 1;
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228 d.hasKnownExtents = true;
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229 d.minValue = 0.0;
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230 d.maxValue = 1.0;
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231 d.isQuantized = false;
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232 d.quantizeStep = 0.0;
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233 d.sampleType = OutputDescriptor::FixedSampleRate;
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234 if (m_stepSize) {
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235 d.sampleRate = m_inputSampleRate / m_stepSize;
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236 } else {
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237 d.sampleRate = m_inputSampleRate / (getPreferredBlockSize()/2);
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238 }
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239 d.hasDuration = false;
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240 list.push_back(d);
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241
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242 d.identifier = "acf";
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243 d.name = "Autocorrelation Function";
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244 d.description = "Autocorrelation of onset detection function";
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245 d.hasKnownExtents = false;
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246 list.push_back(d);
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247
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248 d.identifier = "filtered_acf";
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249 d.name = "Filtered Autocorrelation";
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250 d.description = "Filtered autocorrelation of onset detection function";
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251 list.push_back(d);
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252
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253 return list;
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254 }
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255
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256 FixedTempoEstimator::FeatureSet
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257 FixedTempoEstimator::process(const float *const *inputBuffers, RealTime ts)
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258 {
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259 FeatureSet fs;
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260
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261 if (m_stepSize == 0) {
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262 cerr << "ERROR: FixedTempoEstimator::process: "
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263 << "FixedTempoEstimator has not been initialised"
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264 << endl;
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265 return fs;
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266 }
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267
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268 // if (m_n < m_dfsize) std::cerr << "m_n = " << m_n << std::endl;
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269
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270 if (m_n == 0) m_start = ts;
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271 m_lasttime = ts;
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272
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273 if (m_n == m_dfsize) {
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274 calculate();
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275 fs = assembleFeatures();
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276 ++m_n;
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277 return fs;
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278 }
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279
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280 if (m_n > m_dfsize) return FeatureSet();
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281
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282 int count = 0;
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283
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284 for (size_t i = 1; i < m_blockSize/2; ++i) {
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285
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286 float real = inputBuffers[0][i*2];
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287 float imag = inputBuffers[0][i*2 + 1];
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288
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289 float sqrmag = real * real + imag * imag;
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290
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291 if (m_priorMagnitudes[i] > 0.f) {
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292 float diff = 10.f * log10f(sqrmag / m_priorMagnitudes[i]);
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293 if (diff >= 3.f) ++count;
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294 }
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295
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296 m_priorMagnitudes[i] = sqrmag;
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297 }
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298
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299 m_df[m_n] = float(count) / float(m_blockSize/2);
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300 ++m_n;
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301 return fs;
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302 }
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303
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304 FixedTempoEstimator::FeatureSet
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305 FixedTempoEstimator::getRemainingFeatures()
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306 {
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307 FeatureSet fs;
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308 if (m_n > m_dfsize) return fs;
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309 calculate();
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310 fs = assembleFeatures();
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311 ++m_n;
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312 return fs;
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313 }
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314
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315 float
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316 FixedTempoEstimator::lag2tempo(int lag)
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317 {
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318 return 60.f / ((lag * m_stepSize) / m_inputSampleRate);
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319 }
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320
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321 void
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322 FixedTempoEstimator::calculate()
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323 {
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324 std::cerr << "FixedTempoEstimator::calculate: m_n = " << m_n << std::endl;
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325
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326 if (m_r) {
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327 std::cerr << "FixedTempoEstimator::calculate: calculation already happened?" << std::endl;
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328 return;
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329 }
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330
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331 if (m_n < m_dfsize / 6) {
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332 std::cerr << "FixedTempoEstimator::calculate: Not enough data to go on (have " << m_n << ", want at least " << m_dfsize/4 << ")" << std::endl;
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333 return; // not enough data (perhaps we should return the duration of the input as the "estimated" beat length?)
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334 }
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335
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336 int n = m_n;
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337
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338 m_r = new float[n/2];
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339 m_fr = new float[n/2];
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340
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341 for (int i = 0; i < n/2; ++i) {
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342 m_r[i] = 0.f;
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343 m_fr[i] = 0.f;
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344 }
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345
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cannam@200
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346 for (int i = 0; i < n/2; ++i) {
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347
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cannam@200
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348 for (int j = i; j < n-1; ++j) {
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cannam@200
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349 m_r[i] += m_df[j] * m_df[j - i];
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350 }
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351
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cannam@200
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352 m_r[i] /= n - i - 1;
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353 }
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354
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355 for (int i = 1; i < n/2; ++i) {
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356
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357 m_fr[i] = m_r[i];
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358
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cannam@200
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359 int div = 1;
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360
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cannam@200
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361 int j = i;
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cannam@200
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362
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cannam@200
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363 while (j < n/2) {
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cannam@200
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364 m_fr[i] += m_r[j];
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cannam@200
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365 j *= 2;
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cannam@200
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366 ++div;
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cannam@200
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367 }
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cannam@200
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368 /*
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cannam@200
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369 for (int j = 1; j <= (n/2 - 1)/i; ++j) {
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cannam@200
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370 m_fr[i] += m_r[i * j];
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cannam@200
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371 ++div;
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cannam@200
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372 }
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cannam@200
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373 */
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cannam@200
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374 std::cerr << "i = " << i << ", (n/2 - 1)/i = " << (n/2 - 1)/i << ", sum = " << m_fr[i] << ", div = " << div << ", val = " << m_fr[i] / div << ", t = " << lag2tempo(i) << std::endl;
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cannam@200
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375
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cannam@200
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376
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cannam@200
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377 // m_fr[i] /= 1 + (n/2 - 1)/i;
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cannam@200
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378 m_fr[i] /= div;
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cannam@200
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379 }
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cannam@200
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380
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cannam@200
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381 std::cerr << "FixedTempoEstimator::calculate done" << std::endl;
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cannam@200
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382 }
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cannam@200
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383
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cannam@200
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384
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cannam@198
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385 FixedTempoEstimator::FeatureSet
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cannam@200
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386 FixedTempoEstimator::assembleFeatures()
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cannam@198
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387 {
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cannam@198
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388 FeatureSet fs;
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cannam@200
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389 if (!m_r) return fs; // No results
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cannam@200
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390
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cannam@198
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391 Feature feature;
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cannam@198
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392 feature.hasTimestamp = true;
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cannam@198
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393 feature.hasDuration = false;
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cannam@198
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394 feature.label = "";
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cannam@198
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395 feature.values.clear();
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cannam@198
|
396 feature.values.push_back(0.f);
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cannam@198
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397
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cannam@200
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398 char buffer[40];
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cannam@198
|
399
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cannam@198
|
400 int n = m_n;
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cannam@198
|
401
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cannam@198
|
402 for (int i = 0; i < n; ++i) {
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cannam@198
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403 feature.timestamp = RealTime::frame2RealTime(i * m_stepSize,
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cannam@198
|
404 m_inputSampleRate);
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cannam@200
|
405 feature.values[0] = m_df[i];
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cannam@198
|
406 feature.label = "";
|
cannam@200
|
407 fs[DFOutput].push_back(feature);
|
cannam@198
|
408 }
|
cannam@198
|
409
|
cannam@199
|
410 for (int i = 1; i < n/2; ++i) {
|
cannam@198
|
411 feature.timestamp = RealTime::frame2RealTime(i * m_stepSize,
|
cannam@198
|
412 m_inputSampleRate);
|
cannam@200
|
413 feature.values[0] = m_r[i];
|
cannam@199
|
414 sprintf(buffer, "%.1f bpm", lag2tempo(i));
|
cannam@200
|
415 if (i == n/2-1) feature.label = "";
|
cannam@200
|
416 else feature.label = buffer;
|
cannam@200
|
417 fs[ACFOutput].push_back(feature);
|
cannam@198
|
418 }
|
cannam@198
|
419
|
cannam@198
|
420 float t0 = 60.f;
|
cannam@198
|
421 float t1 = 180.f;
|
cannam@198
|
422
|
cannam@198
|
423 int p0 = ((60.f / t1) * m_inputSampleRate) / m_stepSize;
|
cannam@198
|
424 int p1 = ((60.f / t0) * m_inputSampleRate) / m_stepSize;
|
cannam@198
|
425
|
cannam@200
|
426 // std::cerr << "p0 = " << p0 << ", p1 = " << p1 << std::endl;
|
cannam@198
|
427
|
cannam@198
|
428 int pc = p1 - p0 + 1;
|
cannam@200
|
429 // std::cerr << "pc = " << pc << std::endl;
|
cannam@198
|
430
|
cannam@200
|
431 // int maxpi = 0;
|
cannam@200
|
432 // float maxp = 0.f;
|
cannam@198
|
433
|
cannam@200
|
434 std::map<float, int> candidates;
|
cannam@198
|
435
|
cannam@200
|
436 for (int i = p0; i <= p1 && i < n/2-1; ++i) {
|
cannam@198
|
437
|
cannam@200
|
438 // Only candidates here are those that were peaks in the
|
cannam@200
|
439 // original acf
|
cannam@200
|
440 // if (r[i] > r[i-1] && r[i] > r[i+1]) {
|
cannam@200
|
441 // candidates[filtered] = i;
|
cannam@200
|
442 // }
|
cannam@198
|
443
|
cannam@200
|
444 candidates[m_fr[i]] = i;
|
cannam@198
|
445
|
cannam@198
|
446 feature.timestamp = RealTime::frame2RealTime(i * m_stepSize,
|
cannam@198
|
447 m_inputSampleRate);
|
cannam@200
|
448 feature.values[0] = m_fr[i];
|
cannam@199
|
449 sprintf(buffer, "%.1f bpm", lag2tempo(i));
|
cannam@200
|
450 if (i == p1 || i == n/2-2) feature.label = "";
|
cannam@200
|
451 else feature.label = buffer;
|
cannam@200
|
452 fs[FilteredACFOutput].push_back(feature);
|
cannam@198
|
453 }
|
cannam@198
|
454
|
cannam@200
|
455 // std::cerr << "maxpi = " << maxpi << " for tempo " << lag2tempo(maxpi) << " (value = " << maxp << ")" << std::endl;
|
cannam@198
|
456
|
cannam@200
|
457 if (candidates.empty()) {
|
cannam@200
|
458 std::cerr << "No tempo candidates!" << std::endl;
|
cannam@200
|
459 return fs;
|
cannam@200
|
460 }
|
cannam@198
|
461
|
cannam@198
|
462 feature.hasTimestamp = true;
|
cannam@198
|
463 feature.timestamp = m_start;
|
cannam@198
|
464
|
cannam@198
|
465 feature.hasDuration = true;
|
cannam@198
|
466 feature.duration = m_lasttime - m_start;
|
cannam@198
|
467
|
cannam@200
|
468 std::map<float, int>::const_iterator ci = candidates.end();
|
cannam@200
|
469 --ci;
|
cannam@200
|
470 int maxpi = ci->second;
|
cannam@200
|
471
|
cannam@200
|
472 feature.values[0] = lag2tempo(maxpi);
|
cannam@198
|
473
|
cannam@200
|
474 sprintf(buffer, "%.1f bpm", lag2tempo(maxpi));
|
cannam@199
|
475 feature.label = buffer;
|
cannam@199
|
476
|
cannam@200
|
477 fs[TempoOutput].push_back(feature);
|
cannam@198
|
478
|
cannam@200
|
479 feature.values.clear();
|
cannam@200
|
480 feature.label = "";
|
cannam@200
|
481
|
cannam@200
|
482 while (feature.values.size() < 8) {
|
cannam@200
|
483 feature.values.push_back(lag2tempo(ci->second));
|
cannam@200
|
484 if (ci == candidates.begin()) break;
|
cannam@200
|
485 --ci;
|
cannam@200
|
486 }
|
cannam@200
|
487
|
cannam@200
|
488 fs[CandidatesOutput].push_back(feature);
|
cannam@200
|
489
|
cannam@198
|
490 return fs;
|
cannam@198
|
491 }
|