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Towards detection of perceptually similar sounds: investigating self-organizing maps

Towards detection of perceptually similar sounds: investigating self-organizing maps
Towards detection of perceptually similar sounds: investigating self-organizing maps
This paper outlines a system for the detection of perceptually similar sounds ('sound spotting'), reports on a series of preliminary experiments and discusses their results. The sound spotting system pursues a frame-based approach and consists of three main stages, an auditory model, a self-organizing map and a pattern matching algorithm. The experiments described examine how different types of self-organizing maps classify a set of test sounds preprocessed by an auditory model and evaluate their performance by means of visualizations and quality measures. With these outcomes in mind we suggest directions for the further development of the sound spotting system.
1902956189
45-50
The Society for the Study of Artificial Intelligence and Simulation of Behaviour
Spevak, Christian
20191804-6bdb-4520-a64d-ec0ac4b0512b
Polfreman, Richard
26424c3d-b750-4868-bf6e-2bbb3990df84
Loomes, Martin
3a1e7c38-49f7-4ad1-8820-74632319a22e
Spevak, Christian
20191804-6bdb-4520-a64d-ec0ac4b0512b
Polfreman, Richard
26424c3d-b750-4868-bf6e-2bbb3990df84
Loomes, Martin
3a1e7c38-49f7-4ad1-8820-74632319a22e

Spevak, Christian, Polfreman, Richard and Loomes, Martin (2001) Towards detection of perceptually similar sounds: investigating self-organizing maps. In Proceedings of the AISB'01 Symposium on Creativity in Arts and Sciences. The Society for the Study of Artificial Intelligence and Simulation of Behaviour. pp. 45-50 .

Record type: Conference or Workshop Item (Paper)

Abstract

This paper outlines a system for the detection of perceptually similar sounds ('sound spotting'), reports on a series of preliminary experiments and discusses their results. The sound spotting system pursues a frame-based approach and consists of three main stages, an auditory model, a self-organizing map and a pattern matching algorithm. The experiments described examine how different types of self-organizing maps classify a set of test sounds preprocessed by an auditory model and evaluate their performance by means of visualizations and quality measures. With these outcomes in mind we suggest directions for the further development of the sound spotting system.

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More information

Published date: March 2001
Venue - Dates: AISB'01 Convention, York, United Kingdom, 2001-03-21 - 2001-03-24

Identifiers

Local EPrints ID: 67382
URI: http://eprints.soton.ac.uk/id/eprint/67382
ISBN: 1902956189
PURE UUID: 059c9285-3abf-488c-9a32-17d4d3206bef

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Date deposited: 29 Sep 2009
Last modified: 13 Mar 2024 18:49

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Contributors

Author: Christian Spevak
Author: Martin Loomes

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