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Analyzing auditory representations for sound classification with self-organizing neural networks

Record type: Conference or Workshop Item (Paper)

Three different auditory representations—Lyon’s cochlear model, Patterson’s gammatone filterbank combined with Meddis’ inner hair cell model, and mel-frequency cepstral coefficients—are analyzed in connection with self-organizing maps to evaluate their suitability for a perceptually justified classification of sounds. The self-organizing maps are trained with a uniform set of test sounds preprocessed by the auditory representations. The structure of the resulting feature maps and the trajectories of the individual sounds are visualized and compared to one another. While MFCC proved to be a very efficient representation, the gammatone model produced the most convincing results.

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Citation

Spevak, Christian and Polfreman, Richard, (2000) Analyzing auditory representations for sound classification with self-organizing neural networks Rochesso, Signoretto (ed.) At COST G-6 Conference on Digital Audio Effects (DAFX-00). 07 - 09 Dec 2000. 6 pp, pp. 119-124.

More information

Published date: December 2000
Venue - Dates: COST G-6 Conference on Digital Audio Effects (DAFX-00), 2000-12-07 - 2000-12-09

Identifiers

Local EPrints ID: 67374
URI: http://eprints.soton.ac.uk/id/eprint/67374
PURE UUID: 458b160c-9658-49d6-8464-7984cba99de2

Catalogue record

Date deposited: 29 Sep 2009
Last modified: 19 Jul 2017 00:19

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Contributors

Author: Christian Spevak
Editor: Signoretto Rochesso

University divisions


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