Feature Generation
Question: How to extract features from audio signals (such as music titles or sound samples) that are efficient for a given classification task ?
Our team has pionneered the development of so-called “feature generation” techniques, in the audio domain. Feature generation consists in letting a system evolve features automatically, for a given classification problem, rather than relying on existing feature sets. We have introduced the notion of analytical features, as audio features consisting in mathematical compositions of elementary operators. The EDS system was designed to generate billions of these features and test them, to evolve efficient features and feature sets. We have shown that analytical features perform better than standard features on a series of well-known audio classification problems. We now focus on the mathematical study of the analytical feature space (e.g. is fitness continuous ?) using tools borrowed from complex system theory.
Selected Papers:
Analytical Features: a Knowledge-Based Approach to Audio Feature Generation. EURASIP Journal on Audio, Speech, and Music Processing, 2009(1), February 2009. 
Classification of dog barks: a machine learning approach. Animal Cognition, 11(3):389-400 2008.
Improving the Classification of Percussive Sounds with Analytical Features: a Case Study. Proceedings of Ismir 07, pages 229-232, Vienna, Austria, 2007.
Exploring billions of audio features. In Eurasip, editor, Proceedings of CBMI 07, pages 227-235, Bordeaux, France, 2007.
Automatic Recognition of Urban Sound Sources. Proceedings of the 120th AES Conference, Paris, France, 2006.
Descriptor-based spatialization. Proceedings of AES Conference 2005, Barcelona, Spain, 2005.
Automatic X traditional descriptor extraction: The case of chord recognition.. Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR’2005), pages 444-449, London, U.K., September 2005.
Automatic Extraction of Music Descriptors from Acoustic Signals using EDS. Proceedings of the 116th AES Convention, Berlin, Germany, May 2004.
Extracting Automatically the Perceived Intensity of Music Titles. Proceedings of the 6th COST-G6 Conference on Digital Audio Effects (DAFX03), pages 180-183, London, U.K., September 2003. Queen Mary University
Evolving Automatically High-Level Music Descriptors From Acoustic Signals. Springer Verlag LNCS, 2771:42-53 2003.
Evolving Automatically High-Level Music Descriptors From Acoustic Signals. Sony CSL, 2003.
Participants: François Pachet
Tags: feature extraction, meta-data, Music
References
Exploring billions of audio features. In Eurasip, editor, Proceedings of CBMI 07, pages 227-235, Bordeaux, France, 2007.
Analytical Features: a Knowledge-Based Approach to Audio Feature Generation. EURASIP Journal on Audio, Speech, and Music Processing, 2009(1), February 2009.
Improving the Classification of Percussive Sounds with Analytical Features: a Case Study. Proceedings of Ismir 07, pages 229-232, Vienna, Austria, 2007.
