Acoustic individual identification in a species of field cricket using deep learning.

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Bibliographic Details
Title: Acoustic individual identification in a species of field cricket using deep learning.
Authors: Kabuga E; Centre for Statistics in Ecology, the Environment, and Conservation, University of Cape Town, South Africa.; African Institute for Mathematical Sciences (AIMS South Africa), Cape Town, South Africa., Nandi D; School of Arts and Sciences, Azim Premji University, Bangalore, India., Burrell S; Centre for Research into Ecological and Environmental Modelling, University of Saint Andrews, United Kingdom., Dlamini G; Centre for Statistics in Ecology, the Environment, and Conservation, University of Cape Town, South Africa., Balakrishnan R; Centre for Ecological Sciences, Indian Institute of Science, Bangalore, India., Bah B; African Institute for Mathematical Sciences (AIMS South Africa), Cape Town, South Africa.; Medical Research Council Unit The Gambia at London School of Hygiene and Tropical Medicine, The Gambia., Durbach I; Centre for Statistics in Ecology, the Environment, and Conservation, University of Cape Town, South Africa.; African Institute for Mathematical Sciences (AIMS South Africa), Cape Town, South Africa.; Centre for Research into Ecological and Environmental Modelling, University of Saint Andrews, United Kingdom.
Source: The Journal of the Acoustical Society of America [J Acoust Soc Am] 2026 Jun 01; Vol. 159 (6), pp. 5288-5299.
Publication Type: Journal Article
Journal Info: Publisher: American Institute of Physics Country of Publication: United States NLM ID: 7503051 Publication Model: Print Cited Medium: Internet ISSN: 1520-8524 (Electronic) Linking ISSN: 00014966 NLM ISO Abbreviation: J Acoust Soc Am Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1520-8524
DOI:10.1121/10.0044100