The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Acoustic Identification of Twelve Species of Echolocating Bat By Discriminant Function Analysis and Artificial Neural Networks
Open accessAbstract
We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency–time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79 % with curve-fitting data included, while an artificial neural network achieved 87 %. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.
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Cites
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Cited by
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Marques, T. A., Thomas, L., Martin, S. W., Mellinger, D. K., Ward, J. A., Moretti, D. J., Harris, D., & Tyack, P. L. (2013) · Biological Reviews · doi:10.1111/brv.12001 · Cited by 746
Machine learning in acoustics: Theory and applications
Bianco, M. J., Gerstoft, P., Traer, J., Ozanich, E., Roch, M. A., Gannot, S., & Deledalle, C.-A. (2019) · The Journal of the Acoustical Society of America · doi:10.1121/1.5133944 · Cited by 609
Real-time bioacoustics monitoring and automated species identification
Aide, T. M., Corrada-Bravo, C., Campos-Cerqueira, M., Milan, C., Vega, G., & Alvarez, R. (2013) · PeerJ · doi:10.7717/peerj.103 · Cited by 487
The evolution of echolocation in bats
JONES, G., & TEELING, E. (2006) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2006.01.001 · Cited by 470
Self-citationAutomated classification of bird and amphibian calls using machine learning: A comparison of methods
Acevedo, M. A., Corrada-Bravo, C. J., Corrada-Bravo, H., Villanueva-Rivera, L. J., & Aide, T. M. (2009) · Ecological Informatics · doi:10.1016/j.ecoinf.2009.06.005 · Cited by 317
Metadata
- Authors
- Parsons, S., & Jones, G.
- Venue
- Journal of Experimental Biology
- Publisher
- The Company of Biologists
- Published
- 2000-09-01
- Volume
- 203
- Issue
- 17
- Pages
- 2641-2656
- ISSN
- 0022-0949 (ISSN-L)
- 1477-9145
- OpenAlex
- W2116482884
- Cited by
- 341
- References
- 1
- FWCI
- 7.78
- Type
- journal article
- Language
- en
- Open access
- green
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- Parsons, S., & Jones, G. (2000). Acoustic Identification of Twelve Species of Echolocating Bat By Discriminant Function Analysis and Artificial Neural Networks. Journal of Experimental Biology. https://doi.org/10.1242/jeb.203.17.2641
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