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1 <!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN">
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2 <html>
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3 <head>
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4 <link rel="stylesheet" media="screen" type="text/css" href="/screen.css"/>
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5 <link rel="icon" type="image/png" href="/images/waveform.png"/>
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6 <link rel="shortcut" type="image/png" href="/images/waveform.png"/>
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7 <title>Vamp Example Plugins: User Documentation</title>
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8 <meta name="robots" content="index"/>
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9 </head>
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10 <body>
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11 <h1 id="header"><span>Vamp Example Plugins</span></h1>
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12
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cannam@7
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13 <p>The “vamp-example-plugins” library contains a number of
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14 <a href="http://www.vamp-plugins.org/">Vamp audio analysis
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15 plugins</a> provided as part of the Vamp plugin SDK.
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16
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17 </p>
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18 <p>These are simple, but sometimes useful, plugins whose source code you
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19 are free to study and reuse in any proprietary or non-proprietary
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20 plugins of your own without any licensing obligation.
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21 </p>
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cannam@6
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22 <p>User documentation for the individual plugins in this library follows.
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23 </p>
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24 <div class="toc2">1. <a href="#amplitudefollower">Amplitude Follower</a></div>
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25 <div class="toc2">2. <a href="#fixedtempo">Simple Fixed Tempo Estimator</a></div>
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26 <div class="toc2">3. <a href="#percussiononsets">Simple Percussion Onset Detector</a></div>
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27 <div class="toc2">4. <a href="#powerspectrum">Simple Power Spectrum</a></div>
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28 <div class="toc2">5. <a href="#spectralcentroid">Spectral Centroid</a></div>
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29 <div class="toc2">6. <a href="#zerocrossing">Zero Crossings</a></div>
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30
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31 <div class="oddcontent"><a name="amplitudefollower"></a><h2>1. Amplitude Follower</h2>
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32
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33 <p><b>System identifier</b> – <code>vamp-example-plugins:amplitudefollower</code><br>
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34 <b>RDF URI</b> – <a href="http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#amplitudefollower">http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#amplitudefollower</a>
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35 </p>
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36 <p>Amplitude Follower tracks and returns the amplitude of the audio
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37 signal sample by sample, returning peak values block by block.
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38 </p>
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39 </div><div class="evencontent"><a name="toc2"></a><h3>1.1. Parameters</h3>
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40
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41 <p><b>Attack time</b> (seconds) – The 60dB convergence time for an increase in amplitude.<br>
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42 <b>Release time</b> (seconds) – The 60dB convergence time for a decrease in amplitude.
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43 </p>
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44 <p>For example, if you feed the plugin with a simple step function that
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45 jumps from level A to level B, then the output will start off as A,
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46 then at the moment of stepping it will start to converge exponentially
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47 to B, reaching with 60dB of the actual value within the time specified
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48 by the Attack time parameter.
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49 </p>
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50 <p>Similarly, if the plugin's input then steps down from B to A, the
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51 output will start converging at the moment of stepping, reaching
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52 within 60dB of the new value within the time specified by the Release
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53 time parameter.
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54 </p>
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55 </div><div class="oddcontent"><a name="toc3"></a><h3>1.2. Outputs</h3>
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56
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57 </div><div class="evencontent"><a name="toc4"></a><h4>1.2.1. Amplitude</h4>
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58
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59 <p>The peak tracked amplitude (in volts) for the current processing block.
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60 </p>
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61 </div><div class="oddcontent"><a name="toc5"></a><h3>1.3. References and Credits</h3>
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62
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63 <p>Amplitude Follower uses a method from the SuperCollider audio
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64 processing language. It was implemented as a Vamp plugin by Dan
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65 Stowell.
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66 </p>
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67 </div><div class="evencontent"><a name="fixedtempo"></a><h2>2. Simple Fixed Tempo Estimator</h2>
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68
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69 <p><b>System identifier</b> – <code>vamp-example-plugins:fixedtempo</code><br>
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70 <b>RDF URI</b> – <a href="http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#fixedtempo">http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#fixedtempo</a>
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71 </p>
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72 <p>Simple Fixed Tempo Estimator analyses a fragment of audio and
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73 estimates its tempo. It assumes that its input is of fixed tempo, and
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74 it analyses only the first (small but configurable number of) seconds
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75 before returning a result, discarding all subsequent input.
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76 </p>
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77 <p>The plugin calculates an overall energy rise function across a series
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78 of short frequency-domain input frames, takes the autocorrelation of
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79 this function, filters it to stress possible metrical patterns,
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80 locates peaks, and converts from autocorrelation lag to the
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81 corresponding tempo.
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82 </p>
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83 <p>The filtering process involves searching for peaks at simple
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84 metrically related intervals (at a given autocorrelation lag as well
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85 as at 0.5, 2, and 4 times that lag), boosting each peak that shows
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86 strong related peaks. A simplistic perceptual curve is also applied
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87 in order to increase the probability of detecting a "likely" tempo.
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88 For improved tempo precision, each tempo with strong related peaks is
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89 averaged with the tempi calculated from those peaks.
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90 </p>
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91 <p>The method is best suited for 4/4 pop and dance rhythms.
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92 </p>
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93 <p>This plugin returns many of its intermediate calculations as
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94 additional outputs, as well as the most favoured tempo. Although as a
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95 tempo estimator it's still fairly primitive, it is intended to provide
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96 a useful example of a slightly more complex feature extraction plugin
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97 than the other examples, as well as one that returns several different
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98 types of output at a time.
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99 </p>
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100 </div><div class="oddcontent"><a name="toc7"></a><h3>2.1. Parameters</h3>
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101
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102 <p><b>Minimum estimated tempo</b>, <b>Maximum estimated tempo</b> (bpm) – These
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103 parameters control the range of values within which the tempo
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104 estimator will return its estimate.
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105 </p>
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106 <p><b>Input duration to study</b> (seconds) – The tempo estimator uses only the
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107 first part of its input, discarding any that follows. This parameter
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108 controls how much input it will use. There is no value in increasing
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109 this beyond 8x the duration of the slowest returned beat. The default
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110 of 10 seconds is likely to be appropriate for most purposes.
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111 </p>
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112 </div><div class="evencontent"><a name="toc8"></a><h3>2.2. Outputs</h3>
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113
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114 </div><div class="oddcontent"><a name="toc9"></a><h4>2.2.1. Tempo</h4>
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115
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116 <p>The tempo estimator's best guess at the tempo of its input, in beats
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117 per minute.
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118 </p>
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119 <p>This is returned as a feature whose timestamp and duration cover the
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120 range of the input which was used in estimating the tempo, with a
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121 single value containing the tempo.
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122 </p>
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123 </div><div class="evencontent"><a name="toc10"></a><h4>2.2.2. Tempo candidates</h4>
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124
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125 <p>Several guesses at the possible tempo. This output is returned as a
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126 single feature whose timestamp and duration cover the range of the
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127 input which was used in estimating the tempo, with up to 10 bins
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128 containing one tempo value in each bin, with the "best guess" tempo in
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129 bin 0.
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130 </p>
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131 </div><div class="oddcontent"><a name="toc11"></a><h4>2.2.3. Detection function</h4>
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132
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133 <p>The basic onset detection function used in tempo estimation.
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134 </p>
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135 </div><div class="evencontent"><a name="toc12"></a><h4>2.2.4. Autocorrelation function</h4>
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136
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137 <p>The autocorrelation of the onset detection function.
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138 </p>
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139 </div><div class="oddcontent"><a name="toc13"></a><h4>2.2.5. Filtered Autocorrelation</h4>
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140
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141 <p>The autocorrelation after filtering to boost values with possible
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142 metrically related peaks and to apply perceptual weighting. The peak
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143 value of this function is the one that will be used as the "best
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144 guess".
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145 </p>
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146 </div><div class="evencontent"><a name="toc14"></a><h3>2.3. References and Credits</h3>
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147
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148 <p>Simple Fixed Tempo Estimator uses a method derived from work by
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149 Matthew Davies: see for example M. E. P. Davies and M. D. Plumbley,
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150 <i>Beat Tracking With A Two State Model</i>, in Proceedings of the IEEE
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151 International Conference on Acoustics, Speech and Signal Processing
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152 2005. This plugin, made by Chris Cannam, is only an unsubtle
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153 simplification of a small part of the published method.
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154 </p>
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155 <p>The Queen Mary plugin set
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156 (<a href="http://www.elec.qmul.ac.uk/digitalmusic/downloads/index.html#qm-vamp-plugins">http://www.elec.qmul.ac.uk/digitalmusic/downloads/index.html#qm-vamp-plugins</a>)
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157 contains a Tempo and Beat Tracker plugin by Matthew Davies providing a
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158 more realistic implementation.
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159 </p>
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160 </div><div class="oddcontent"><a name="percussiononsets"></a><h2>3. Simple Percussion Onset Detector</h2>
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161
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162 <p><b>System identifier</b> – <code>vamp-example-plugins:percussiononsets</code><br>
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163 <b>RDF URI</b> – <a href="http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#percussiononsets">http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#percussiononsets</a>
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164 </p>
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165 <p>Simple Percussion Onset Detector estimates the locations of percussive
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166 onsets in the audio signal.
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167 </p>
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168 <p>The principle is to exploit the broadband nature of noisy percussive
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169 onsets by identifying only those frames in which the energy rise shows
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170 a broadband profile.
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171 </p>
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172 <p>The plugin takes a series of frequency domain frames, and examines
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173 each frame to count the number of bins whose energy content has
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174 increased by more than a certain threshold since the prior frame.
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175 Frames in which this number is at a peak relative to prior and
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176 following frames and also exceeds another threshold value are
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177 classified as percussive onsets.
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178 </p>
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179 </div><div class="evencontent"><a name="toc16"></a><h3>3.1. Parameters</h3>
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180
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181 <p><b>Energy rise threshold</b> (dB) – The rise in energy within a bin from one
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182 frame to the next that is required for a bin to be counted toward the
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183 detection function's bin count. This roughly corresponds to how
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184 "loud" a percussive sound must be in order to be detected.
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185 </p>
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186 <p><b>Sensitivity</b> (%) – The proportion of bins that must exceed the energy
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187 rise threshold in order for an onset to be detected (at frames in
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188 which the detection function peaks). This roughly corresponds to how
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189 "noisy" a percussive sound must be in order to be detected.
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190 </p>
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191 </div><div class="oddcontent"><a name="toc17"></a><h3>3.2. Outputs</h3>
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192
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193 </div><div class="evencontent"><a name="toc18"></a><h4>3.2.1. Onsets</h4>
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194
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195 <p>The estimated onset locations.
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196 </p>
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197 </div><div class="oddcontent"><a name="toc19"></a><h4>3.2.2. Detection Function</h4>
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198
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199 <p>The energy rise detection function whose peaks were used to estimate
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200 onset locations.
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201 </p>
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202 </div><div class="evencontent"><a name="toc20"></a><h3>3.3. References and Credits</h3>
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203
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204 <p>The method used in Simple Percussion Onset Detector was described in
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205 Dan Barry, Derry Fitzgerald, Eugene Coyle and
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206 Bob Lawlor, <i>Drum Source Separation using Percussive Feature Detection and
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207 Spectral Modulation</i>, ISSC 2005. The plugin was made by Chris Cannam.
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208 </p>
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209 </div><div class="oddcontent"><a name="powerspectrum"></a><h2>4. Simple Power Spectrum</h2>
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210
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211 <p><b>System identifier</b> – <code>vamp-example-plugins:powerspectrum</code><br>
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212 <b>RDF URI</b> – <a href="http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#powerspectrum">http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#powerspectrum</a>
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213 </p>
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214 <p>Simple Power Spectrum returns a power spectrum calculated from
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215 windowed short-time Fourier transforms of the input audio. (The power
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216 spectrum for a frame consists of a sequence of the squares of the
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217 magnitudes of the complex values for each frequency bin in the result
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218 of the Fourier transform.)
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219 </p>
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220 <p>This very simple plugin is an illustration of the fact that if a
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221 plugin requests frequency-domain input, its input will already be in
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222 the form needed for a spectrum such as this. The plugin has no work
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223 left to do except to calculate the squared magnitude from the
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224 cartesian complex representation.
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225 </p>
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226 <p>This plugin also illustrates how to return "grid-type" visualisation
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227 data from a Vamp plugin.
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228 </p>
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229 </div><div class="evencontent"><a name="toc22"></a><h3>4.1. Parameters</h3>
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230
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231 <p>None.
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232 </p>
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233 </div><div class="oddcontent"><a name="toc23"></a><h3>4.2. Outputs</h3>
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234
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235 </div><div class="evencontent"><a name="toc24"></a><h4>4.2.1. Power Spectrum</h4>
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236
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237 <p>The power spectrum calculated from the input frame. This output
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238 returns a single feature per processing block, containing
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239 blocksize/2+1 power values corresponding to the FFT bins from DC to
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240 Nyquist inclusive. The DC bin is always returned.
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241 </p>
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242 </div><div class="oddcontent"><a name="spectralcentroid"></a><h2>5. Spectral Centroid</h2>
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243
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244 <p><b>System identifier</b> – <code>vamp-example-plugins:spectralcentroid</code><br>
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245 <b>RDF URI</b> – <a href="http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#spectralcentroid">http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#spectralcentroid</a>
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246 </p>
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247 <p>Spectral Centroid calculates the "centre of gravity" of the frequency
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248 spectrum for each input frame.
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249 </p>
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250 </div><div class="evencontent"><a name="toc26"></a><h3>5.1. Parameters</h3>
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251
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252 <p>None.
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253 </p>
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254 </div><div class="oddcontent"><a name="toc27"></a><h3>5.2. Outputs</h3>
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255
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256 </div><div class="evencontent"><a name="toc28"></a><h4>5.2.1. Log Frequency Centroid</h4>
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257
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258 <p>The centroid of the log-weighted frequency spectrum. That is, the sum
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259 across Fourier transform output bins of the logarithm of the bin
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260 frequency multiplied by the bin magnitude, divided by the sum of the
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261 bin magnitudes, and the inverse logarithm taken so as to give the
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262 result as a frequency in Hz.
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263 </p>
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264 </div><div class="oddcontent"><a name="toc29"></a><h4>5.2.2. Linear Frequency Centroid</h4>
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265
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266 <p>The centroid of the linear-weighted frequency spectrum. That is, the
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267 sum across Fourier transform output bins of the bin frequency
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268 multiplied by the bin magnitude, divided by the sum of the bin
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269 magnitudes. The result is a frequency in Hz.
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270 </p>
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271 </div><div class="evencontent"><a name="zerocrossing"></a><h2>6. Zero Crossings</h2>
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272
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273 <p><b>System identifier</b> – <code>vamp-example-plugins:zerocrossing</code><br>
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274 <b>RDF URI</b> – <a href="http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#zerocrossing">http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#zerocrossing</a>
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275 </p>
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276 <p>Zero Crossings calculates the positions and density of "zero-crossing"
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277 points in an audio waveform. For the purposes of this plugin, that
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278 means those positions at which the sampled value switches from
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279 zero-or-less to greater-than-zero, or vice versa.
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280 </p>
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281 </div><div class="oddcontent"><a name="toc31"></a><h3>6.1. Parameters</h3>
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282
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283 <p>None.
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284 </p>
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285 </div><div class="evencontent"><a name="toc32"></a><h3>6.2. Outputs</h3>
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286
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287 </div><div class="oddcontent"><a name="toc33"></a><h4>6.2.1. Zero Crossing Counts</h4>
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288
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289 <p>The number of zero-crossing points found in the current block of
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290 samples, as a single-valued feature returned per processing block.
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291 </p>
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292 </div><div class="evencontent"><a name="toc34"></a><h4>6.2.2. Zero Crossings</h4>
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293
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294 <p>The locations of zero-crossing points, returning one feature
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295 timestamped to the zero-crossing location, without values, for each
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296 crossing point.
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297 </p>
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298 <p></p>
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299
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300 </div>
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301 </body>
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302 </html>
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