Vamp Plugins

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The Vamp audio analysis plugin system

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Vamp is an audio processing plugin system for cannam@0: plugins that extract descriptive information from audio data cannam@0: — typically referred to as audio analysis plugins cannam@0: or audio feature extraction plugins.

cannam@0: cannam@31: Freehand sketch of a Vamp plugin in its natural habitat

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Get some plugins!

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Find links to downloadable Vamp plugins here.

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Do something with them!

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Interactive audio analysis using Vamp plugins? Try Sonic Visualiser!
Analysis inside your audio editor? Audacity can use a number of Vamp plugins to create label tracks from analysis results.
Want to extract features from lots of audio files, writing results into text files or as RDF for linked data purposes? You need Sonic Annotator!
For simple command-line purposes, there is also a Vamp host included in the developer SDK.
... any other hosts you'd like to see mentioned here? Tell us!

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Developers and DSP researchers!

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Vamp is a nice API to develop with. Read more and get the SDK here.
Not so keen on C++? You can now write Vamp plugins in Python! Find out more here.
Like to use native Vamp plugins in a Java application? Take a look at jVamp.

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Just like an audio effect such as a VST plugin, a Vamp plugin Chris@113: is a binary module that can be loaded by a host application and Chris@113: fed audio data. However, a Vamp plugin does not generate audio Chris@113: output, but some sort of symbolic information instead. Typical Chris@113: things that a Vamp plugin might calculate include the locations Chris@113: of moments such as note onset times, visualisable Chris@113: representations of the audio such as spectrograms, or curve data Chris@113: such as power or fundamental cannam@21: frequency. Read more about the cannam@21: rationale for Vamp.

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	Developed at the Centre for Digital Music, Queen Mary, University of London.	Partially funded by the EPSRC through the OMRAS2 project EP/E017614/1.
	Partially funded by the European Commission through the SIMAC project IST-FP6-507142.	Partially funded by the European Commission through the EASAIER project IST-FP6-033902.

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