FEAPI

FEAPI

Infobox_Software
name = FEAPI
developer = G. Eisenberg, A. Lerch, R. Muller, K. Tanghe, G. Volpe
latest_release_version = 1.0rc1
latest_release_date = June 20, 2005
operating_system = independent
genre = Feature extraction
license = [http://svn.sourceforge.net/viewcvs.cgi/feapi/trunk/FEAPI/license.txt?view=markup BSD license]
website = [http://feapi.sourceforge.net/ feapi.sourceforge.net]

Context
Audio analysis using machine learning methods for classification begins with an extraction of suitable features. Well-known methods for extraction of audio features are often re-implemented by many researchers working in the field of audio analysis. The low level feature extraction plugin API (FEAPI) provides a generic interface for audio feature extraction modules.

History
At the beginning of January 2005, Alexander Lerch posted a message on the music-ir mailing list with the idea (and a first draft) for a generic audio feature extraction plugin API, openly sollicitating feedback and collaboration from other R&D engineers working in this field. The idea was to make it an easy-to-use, open, platform-independent and very permissively licensed API that could be used as a common interface for all parties creating or using low-level audio feature extraction software modules.

Capabilities
The capabilities of FEAPI were defined [Alexander Lerch, Gunnar Eisenberg, Koen Tanghe: " [http://feapi.sourceforge.net/DAFx05_FEAPI_A_Low_Level_Feature_Extraction_Plugin_API.pdf FEAPI: A Low Level Feature Extraction Plugin API] ", 8th International Conference on Digital Audio Effects (DAFx), Madrid, Spain, 2005] as:
* support for different and possibly varying sample rates of the extracted features
* support for multiple independent instances of each plugin
* support for multidimensional features
* push-style processing of audio buffers (data source can be anything: files, live streams, ...)
* support for sufficient timing information to allow synchronization of features with different sample rates
* support for the calculation of multiple features in one plugin, if required by the developer
* high probability of unique plugin identification by the host without a registration process

License
The FEAPI code is licensed under a BSD style license, which makes it usable in both open or closed source applications, commercial and non-commercial.

References

ee also

* Features (pattern recognition)
* Feature extraction
* Machine learning
* Pattern recognition

External links

* [http://feapi.sourceforge.net/ official FEAPI website]
* [http://listes.ircam.fr/wws/info/music-ir music-ir mailing list]


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