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Analytical Features: A Knowledge-Based Approach to Audio Feature Generation

Pachet, François and Roy, Pierre. 2009. Analytical Features: A Knowledge-Based Approach to Audio Feature Generation. EURASIP Journal on Audio, Speech, and Music Processing, 2009, n/a-n/a. ISSN 1687-4722 [Article]

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Abstract or Description

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.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1155/2009/153017

Departments, Centres and Research Units:

Computing
Research Office > REF2014

Dates:

DateEvent
2009Published

Item ID:

9517

Date Deposited:

18 Nov 2013 12:55

Last Modified:

18 Feb 2015 01:26

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/9517

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