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A context aware sound classifier applied to prawn feed monitoring and energy disaggregation

journal contribution
posted on 2023-05-19, 07:09 authored by Smith, DV, Shahriar, MS
Two sound classifiers were proposed for a novel aquaculture application that involved processing sound to estimate the feed consumption of prawns within the turbid waters of farm ponds. A two stage content classifier inferred feed events using identified sound features. To deal with the class ambiguity created by the acoustically challenging conditions of ponds, the CADBN was proposed to jointly model the sound features with the context of feed events. The CADBN was then reformulated to classify the energy load of devices using a distributed state space that enabled flexible and efficient modelling of context. The CADBN was compared to a set of benchmark classifiers for both the prawn feeding and energy applications. Results indicate that the inclusion of context greatly enhances class discrimination in both problems. Furthermore, results illustrate that the temporal structure of the CADBN produced superior performance to benchmark context classifiers that adopt the same context features as independent inputs.

History

Publication title

Knowledge-Based Systems

Volume

52

Pagination

21-31

ISSN

0950-7051

Department/School

School of Information and Communication Technology

Publisher

Elsevier Science Bv

Place of publication

Po Box 211, Amsterdam, Netherlands, 1000 Ae

Rights statement

Crown Copyright 2013

Repository Status

  • Restricted

Socio-economic Objectives

Aquaculture prawns

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