Analytical Features: A Knowledge-Based Approach to Audio Feature Generation. (8th April 2009)

• Record Type: Journal Article
• Authors: Pachet, François; Roy, Pierre
• Other Names: Heusdens Richard Academic Editor.
• Is Part Of: EURASIP journal on audio, speech, and music processing. Volume 2009(2009)
• Publication Date: 2009-04-08
• Subjects: Sound -- Recording and reproducing -- Digital techniques -- Periodicals; Computer sound processing -- Periodicals; Computer sound processing; Sound -- Recording and reproducing -- Digital techniques; Periodicals; Electronic journal; Electronic journals; 620.2
• DOI: 10.1155/2009/153017
• Languages: English
• ISSNs: 1687-4714

Abstract

Abstract : We present a feature generation system designed to create audio features for supervised classification tasks. The main contribution to feature generation studies is the notion of analytical features (AFs), a construct designed to support the representation of knowledge about audio signal processing. We describe the most important aspects of AFs, in particular their dimensional type system, on which are based pattern-based random generators, heuristics, and rewriting rules. We show how AFs generalize or improve previous approaches used in feature generation. We report on several projects using AFs for difficult audio classification tasks, demonstrating their advantage over standard audio features. More generally, we propose analytical features as a paradigm to bring raw signals into the world of symbolic computation.