The vamp-example-plugins library contains a number of
+ Vamp audio analysis
+ plugins provided as part of the Vamp plugin SDK.
+
+
These are simple, but sometimes useful, plugins whose source code you + are free to study and reuse in any proprietary or non-proprietary + plugins of your own without any licensing obligation. +
+User documentation for the individual plugins in this library follows. +
+ + + + + + + +System identifier – vamp-example-plugins:amplitudefollower
+ RDF URI – http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#amplitudefollower
+
Amplitude Follower tracks and returns the amplitude of the audio + signal sample by sample, returning peak values block by block. +
+Attack time (seconds) – The 60dB convergence time for an increase in amplitude.
+ Release time (seconds) – The 60dB convergence time for a decrease in amplitude.
+
For example, if you feed the plugin with a simple step function that + jumps from level A to level B, then the output will start off as A, + then at the moment of stepping it will start to converge exponentially + to B, reaching with 60dB of the actual value within the time specified + by the Attack time parameter. +
+Similarly, if the plugin's input then steps down from B to A, the + output will start converging at the moment of stepping, reaching + within 60dB of the new value within the time specified by the Release + time parameter. +
+Amplitude Follower uses a method from the SuperCollider audio + processing language. It was implemented as a Vamp plugin by Dan + Stowell. +
+System identifier – vamp-example-plugins:fixedtempo
+ RDF URI – http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#fixedtempo
+
Simple Fixed Tempo Estimator analyses a fragment of audio and + estimates its tempo. It assumes that its input is of fixed tempo, and + it analyses only the first (small but configurable number of) seconds + before returning a result, discarding all subsequent input. +
+The plugin calculates an overall energy rise function across a series + of short frequency-domain input frames, takes the autocorrelation of + this function, filters it to stress possible metrical patterns, + locates peaks, and converts from autocorrelation lag to the + corresponding tempo. +
+The filtering process involves searching for peaks at simple + metrically related intervals (at a given autocorrelation lag as well + as at 0.5, 2, and 4 times that lag), boosting each peak that shows + strong related peaks. A simplistic perceptual curve is also applied + in order to increase the probability of detecting a "likely" tempo. + For improved tempo precision, each tempo with strong related peaks is + averaged with the tempi calculated from those peaks. +
+The method is best suited for 4/4 pop and dance rhythms. +
+This plugin returns many of its intermediate calculations as + additional outputs, as well as the most favoured tempo. Although as a + tempo estimator it's still fairly primitive, it is intended to provide + a useful example of a slightly more complex feature extraction plugin + than the other examples, as well as one that returns several different + types of output at a time. +
+Minimum estimated tempo, Maximum estimated tempo (bpm) – These + parameters control the range of values within which the tempo + estimator will return its estimate. +
+Input duration to study (seconds) – The tempo estimator uses only the + first part of its input, discarding any that follows. This parameter + controls how much input it will use. There is no value in increasing + this beyond 8x the duration of the slowest returned beat. The default + of 10 seconds is likely to be appropriate for most purposes. +
+The tempo estimator's best guess at the tempo of its input, in beats + per minute. +
+This is returned as a feature whose timestamp and duration cover the + range of the input which was used in estimating the tempo, with a + single value containing the tempo. +
+Several guesses at the possible tempo. This output is returned as a + single feature whose timestamp and duration cover the range of the + input which was used in estimating the tempo, with up to 10 bins + containing one tempo value in each bin, with the "best guess" tempo in + bin 0. +
+The autocorrelation after filtering to boost values with possible + metrically related peaks and to apply perceptual weighting. The peak + value of this function is the one that will be used as the "best + guess". +
+Simple Fixed Tempo Estimator uses a method derived from work by + Matthew Davies: see for example M. E. P. Davies and M. D. Plumbley, + Beat Tracking With A Two State Model, in Proceedings of the IEEE + International Conference on Acoustics, Speech and Signal Processing + 2005. This plugin, made by Chris Cannam, is only an unsubtle + simplification of a small part of the published method. +
+The Queen Mary plugin set + (http://www.elec.qmul.ac.uk/digitalmusic/downloads/index.html#qm-vamp-plugins) + contains a Tempo and Beat Tracker plugin by Matthew Davies providing a + more realistic implementation. +
+System identifier – vamp-example-plugins:percussiononsets
+ RDF URI – http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#percussiononsets
+
Simple Percussion Onset Detector estimates the locations of percussive + onsets in the audio signal. +
+The principle is to exploit the broadband nature of noisy percussive + onsets by identifying only those frames in which the energy rise shows + a broadband profile. +
+The plugin takes a series of frequency domain frames, and examines + each frame to count the number of bins whose energy content has + increased by more than a certain threshold since the prior frame. + Frames in which this number is at a peak relative to prior and + following frames and also exceeds another threshold value are + classified as percussive onsets. +
+Energy rise threshold (dB) – The rise in energy within a bin from one + frame to the next that is required for a bin to be counted toward the + detection function's bin count. This roughly corresponds to how + "loud" a percussive sound must be in order to be detected. +
+Sensitivity (%) – The proportion of bins that must exceed the energy + rise threshold in order for an onset to be detected (at frames in + which the detection function peaks). This roughly corresponds to how + "noisy" a percussive sound must be in order to be detected. +
+The energy rise detection function whose peaks were used to estimate + onset locations. +
+The method used in Simple Percussion Onset Detector was described in +Dan Barry, Derry Fitzgerald, Eugene Coyle and + Bob Lawlor, Drum Source Separation using Percussive Feature Detection and + Spectral Modulation, ISSC 2005. The plugin was made by Chris Cannam. +
+System identifier – vamp-example-plugins:powerspectrum
+ RDF URI – http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#powerspectrum
+
Simple Power Spectrum returns a power spectrum calculated from + windowed short-time Fourier transforms of the input audio. (The power + spectrum for a frame consists of a sequence of the squares of the + magnitudes of the complex values for each frequency bin in the result + of the Fourier transform.) +
+This very simple plugin is an illustration of the fact that if a + plugin requests frequency-domain input, its input will already be in + the form needed for a spectrum such as this. The plugin has no work + left to do except to calculate the squared magnitude from the + cartesian complex representation. +
+This plugin also illustrates how to return "grid-type" visualisation + data from a Vamp plugin. +
+The power spectrum calculated from the input frame. This output + returns a single feature per processing block, containing + blocksize/2+1 power values corresponding to the FFT bins from DC to + Nyquist inclusive. The DC bin is always returned. +
+System identifier – vamp-example-plugins:spectralcentroid
+ RDF URI – http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#spectralcentroid
+
Spectral Centroid calculates the "centre of gravity" of the frequency + spectrum for each input frame. +
+The centroid of the log-weighted frequency spectrum. That is, the sum + across Fourier transform output bins of the logarithm of the bin + frequency multiplied by the bin magnitude, divided by the sum of the + bin magnitudes, and the inverse logarithm taken so as to give the + result as a frequency in Hz. +
+The centroid of the linear-weighted frequency spectrum. That is, the + sum across Fourier transform output bins of the bin frequency + multiplied by the bin magnitude, divided by the sum of the bin + magnitudes. The result is a frequency in Hz. +
+System identifier – vamp-example-plugins:zerocrossing
+ RDF URI – http://vamp-plugins.org/rdf/plugins/vamp-example-plugins#zerocrossing
+
Zero Crossings calculates the positions and density of "zero-crossing" + points in an audio waveform. For the purposes of this plugin, that + means those positions at which the sampled value switches from + zero-or-less to greater-than-zero, or vice versa. +
+The number of zero-crossing points found in the current block of + samples, as a single-valued feature returned per processing block. +
+The locations of zero-crossing points, returning one feature + timestamped to the zero-crossing location, without values, for each + crossing point. +
+ + +