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


Spevak, Christian and Polfreman, Richard (2000) Analyzing auditory representations for sound classification with self-organizing neural networks. In, COST G-6 Conference on Digital Audio Effects (DAFX-00), Verona, Italy, 07 - 09 Dec 2000. Verona, Italy, Universita degli Studi di Verona6pp, 119-124.

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Description/Abstract

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.

Item Type: Conference or Workshop Item (Paper)
Related URLs:
Subjects: T Technology > T Technology (General)
Q Science > QC Physics
M Music and Books on Music > ML Literature of music
Divisions: University Structure - Pre August 2011 > School of Humanities > Music
ePrint ID: 67374
Date Deposited: 29 Sep 2009
Last Modified: 27 Mar 2014 18:48
URI: http://eprints.soton.ac.uk/id/eprint/67374

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