view examples/vamp-example-plugins.txt @ 257:f80e34e36a79

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Vamp Example Plugins
====================

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.

Amplitude Follower
==================

*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.

Parameters
----------

*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.

Outputs
-------
Amplitude
~~~~~~~~~
The peak tracked amplitude (in volts) for the current processing block.

References and Credits
----------------------
Amplitude Follower uses a method from the SuperCollider audio
processing language.  It was implemented as a Vamp plugin by Dan
Stowell.


Simple Fixed Tempo Estimator
============================

*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.

Parameters
----------

*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.

Outputs
-------

Tempo
~~~~~

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.

Tempo candidates
~~~~~~~~~~~~~~~~

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.

Detection function
~~~~~~~~~~~~~~~~~~

The basic onset detection function used in tempo estimation.

Autocorrelation function
~~~~~~~~~~~~~~~~~~~~~~~~

The autocorrelation of the onset detection function.

Filtered Autocorrelation
~~~~~~~~~~~~~~~~~~~~~~~~

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".

References and Credits
----------------------
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 very 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.


Simple Percussion Onset Detector
================================

*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.

Parameters
----------

*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.

Outputs
-------

Onsets
~~~~~~

The estimated onset locations.

Detection Function
~~~~~~~~~~~~~~~~~~

The energy rise detection function whose peaks were used to estimate
onset locations.

References and Credits
----------------------
The method used in Simple Percussion Onset Detector was described in
"Drum Source Separation using Percussive Feature Detection and
Spectral Modulation" by Dan Barry, Derry Fitzgerald, Eugene Coyle and
Bob Lawlor, ISSC 2005.  The plugin was made by Chris Cannam.


Simple Power Spectrum
=====================

*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.

Parameters
----------

None.

Outputs
-------

Power Spectrum
~~~~~~~~~~~~~~

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.


Spectral Centroid
=================

*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.

Parameters
----------

None.

Outputs
-------

Log Frequency Centroid
~~~~~~~~~~~~~~~~~~~~~~

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.

Linear Frequency Centroid
~~~~~~~~~~~~~~~~~~~~~~~~~

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.


Zero Crossings
==============

*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.

Parameters
----------

None.

Outputs
-------

Zero Crossing Counts
~~~~~~~~~~~~~~~~~~~~

The number of zero-crossing points found in the current block of
samples, as a single-valued feature returned per processing block.

Zero Crossings
~~~~~~~~~~~~~~

The locations of zero-crossing points, returning one feature
timestamped to the zero-crossing location, without values, for each
crossing point.