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Automated identification of field-recorded songs of four British grasshoppers using bioacoustic signal recognition

Published online by Cambridge University Press:  09 March 2007

E.D. Chesmore  and
E. Ohya
E.D. Chesmore*
Affiliation:
Department of Electronics, University of York, Heslington, York, YO10 5DD, UK
E. Ohya
Affiliation:
Biodiversity Research Group, Tohoku Research Center, Forestry and Forest Products Research Institute, Shimokuriyagawa aza Nabeyashiki 92–25, Morioka 020–0123, Japan
*
*Fax: 01904 432335 E-mail: edc1@ohm.york.ac.uk
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Abstract

Recognition of Orthoptera species by means of their song is widely used in field work but requires expertise. It is now possible to develop computer-based systems to achieve the same task with a number of advantages including continuous long term unattended operation and automatic species logging. The system described here achieves automated discrimination between different species by utilizing a novel time domain signal coding technique and an artificial neural network. The system has previously been shown to recognize 25 species of British Orthoptera with 99% accuracy for good quality sounds. This paper tests the system on field recordings of four species of grasshopper in northern England in 2002 and shows that it is capable of not only correctly recognizing the target species under a range of acoustic conditions but also of recognizing other sounds such as birds and man-made sounds. Recognition accuracies for the four species of typically 70–100% are obtained for field recordings with varying sound intensities and background signals.

Information

Type
Research Article
Information
Bulletin of Entomological Research , Volume 94 , Issue 4 , August 2004 , pp. 319 - 330
Copyright
Copyright © Cambridge University Press 2004

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References

Anderson, S.E., Dave, A.S. & Margoliash, D. (1996) Template-based automatic recognition of birdsong syllables from continuous recordings. Journal of the Acoustical Society of America 100, 1209–1217.CrossRefGoogle ScholarPubMed
Bishop, C.M. (2000) Neural networks for pattern recognition, Oxford, Oxford University Press.Google Scholar
Campbell, R.H., Martin, S.K., Schneider, I. & Michelson, W.R. (1996) Analysis of mosquito wing beat sound. 132nd Meeting of the Acoustical Society of America HonoluluCrossRefGoogle Scholar
Chesmore, E.D. (1999) Technology transfer: applications of electronic technology in ecology and entomology for species identification. Natural History Research 5, 111–126.Google Scholar
Chesmore, E.D. (2000) Methodologies for automating the identification of species. pp. 3–12 in Proceedings of 1st BioNET International Working Group on Automated TaxonomyJuly 1997.Google Scholar
Chesmore, E.D. (2001) Application of time domain signal coding and artificial neural networks to passive acoustical identification of animals. Applied Acoustics 62, 1359–1374.CrossRefGoogle Scholar
Chesmore, E.D. & Nellenbach, C. (2001) Acoustic methods for the automated detection and identification of insects. Acta Horticulturae 562, 223–231.CrossRefGoogle Scholar
Chesmore, E.D., Swarbrick, M.D. & Femminella, O.P. (1998) Automated analysis of insect sounds using TESPAR and expert systems–a new method for species identification. pp. 273–287. Wallingford, Oxon, CAB International.Google Scholar
Clemins, P.J. & Johnson, M.T. (2002) Automatic type classification and speaker verification of African elephant vocalizations. Animal Behaviour 2002 Vrije Universiteit AmsterdamGoogle Scholar
Haack, R.A., Law, K.R., Mastro, V.C., Ossenbruggen, H.S. & Raimo, B.J. (1997) New York's battle with the Asian long-horned beetle. Journal of Forestry 95, 11–15.Google Scholar
Hagstrum, D.W., Vick, K.W. & Webb, J.C. (1990) Acoustic monitoring of Rhizopertha dominica (Coleoptera: Bostrichidae) populations in stored wheat. Journal of Economic Entomology 83, 625–628.CrossRefGoogle Scholar
Hickling, R., Lee, P., Velea, D., Denneby, T.J. & Patin, A.I. (2000) Acoustic system for rapidly detecting and monitoring pink bollworm in cotton bolls. Proceedings of the Beltwide Cotton Conference 1–10 San AntonioGoogle Scholar
King, R.A. & Gosling, W. (1978) Time-encoded speech. Electronics Letters 15, 1456–1457.Google Scholar
Looney, C.G. (1997) Pattern recognition using neural networks, Oxford, Oxford University Press.Google Scholar
Lucking, W.G., Darnell, M. & Chesmore, E.D. (1994) Acoustical condition monitoring of a mechanical gearbox using artificial neural networks. 120–124 in IEEE Conference on Neural NetworksOrlando, Florida.Google Scholar
MacLeod, A., Evans, H.F., Baker, R.H.A. (2002) An analysis of pest risk from an Asian longhorn beetle (Anoplophora glabripennis) to hardwood trees in the European community. Crop Protection 21, 635–645.CrossRefGoogle Scholar
Mankin, R.W. & Weaver, D.K. (2000) Acoustic detection and monitoring of Cephus cinctus sawfly larvae in wheat stems. International Congress of Entomology BrazilGoogle Scholar
Mankin, R.W., Brandhurst-Hubbard, J., Flanders, K.L., Zhang, M., Crocker, R.L., Lapointe, S.L., McCoy, C.W., Fisher, J.R. & Weaver, D.K. (2000) Eavesdropping on insects hidden in soil and interior structures of plants. Journal of Economic Entomology 93, 1173–1182.CrossRefGoogle ScholarPubMed
Matsuoka, H., Fujii, Y., Okumura, S., Imamura, Y. & Yoshimura, T. (1996) Relationship between the type of feeding behaviour of termites and the acoustic emission (AE) generation. Wood Research 83, 1–7.Google Scholar
McIlraith, A.L. & Card, H.C. (1995) Birdsong recognition with DSP and neural networks. pp. 409–414 in IEEE WESCANEX'95 ProceedingsWinnipeg, CanadaGoogle Scholar
Mills, H. (1995) Automatic detection and classification of nocturnal migrant bird calls. Journal of the Acoustical Society of America 97, 3370–3371.CrossRefGoogle Scholar
Murray, S.O., Mercado, E. & Roitblat, H.L. (1998) The neural network classification of false killer whale (Pseudorca crassidens) vocalizations. Journal of the Acoustical Society of America 104, 3626–3633.CrossRefGoogle ScholarPubMed
Ohya, E. & Chesmore, E.D. (2003) Automated identification of grasshoppers by their songs. Annual Meeting of the Japanese Society of Applied Entomology and Zoology Iwate University, Morioka Japan.Google Scholar
Parsons, S. (2001) Identification of New Zealand bats in flight from analysis of echolocation calls by artificial neural networks. Journal of Zoology 253, 447–456.CrossRefGoogle Scholar
Parsons, S. & Jones, G. (2000) Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks. Journal of Experimental Biology 203, 2641–2656.CrossRefGoogle ScholarPubMed
Reby, D., Lek, S., Dimopoulos, I., Joachim, J., Lauga, J. & Aulagnier, S. (1997) Artificial neural networks as a classification method in the behavioural sciences. Behavioural Processes 40, 35–43.CrossRefGoogle ScholarPubMed
Riede, K. (1993) Monitoring biodiversity: analysis of Amazonian rainforest sounds. Ambio 22, 546–548.Google Scholar
Schalkoff, R. (1992) Pattern recognition: statistical, structural and neural approaches New York John Wiley & SonsGoogle Scholar
Schwenker, F., Dietrich, C., Kestler, H.A., Riede, K. & Palm, G. (2003) Radial basis function neural networks and temporal fusion for the classification of bioacoustic time series. Neurocomputing 51, 265–275.CrossRefGoogle Scholar
Shuman, D., Coffelt, J.A., Vick, K.W. & Mankin, R.W. (1993) Quantitative acoustical detection of larvae feeding inside kernels of grain. Journal of Economic Entomology 86, 933–938.CrossRefGoogle Scholar
Shuman, D., Weaver, D.K. & Mankin, R.W. (1997) Quantifying larval infestation with an acoustical sensor array and cluster analysis of cross-correlation outputs. Applied Acoustics 50, 279–296.CrossRefGoogle Scholar
Swarbrick, M.D. (2001) Acoustic diagnosis of heart defects using time-domain signal processing and artificial neural networks. PhD thesis, University of HullGoogle Scholar
Swarbrick, M.D. & Chesmore, E.D. (1998) Automatic classification of heart sounds and murmurs using time domain signal coding (TDSC), artificial neural networks and expert systems. pp. 37–46 in Proceedings, Conference on Recent Advances in Soft Computing 98DeMontfort University.Google Scholar
Taylor, A., Grigg, G., Watson, G., McCallum, H. (1996) Monitoring frog communities, an application of machine learning. Eighth Innovative Applications of Artificial Intelligence Conference 1996AAAI Press.Google Scholar
Terry, A.M.R., McGregor, P.K. (2002) Census and monitoring based on individually identifiable vocalizations: the role of neural networks. Animal Conservation 5, 103–111.CrossRefGoogle Scholar
Vaughan, N., Jones, G. & Harris, S. (1996) Identification of British bat species by multivariate analysis of echolocation call parameters. Bioacoustics 7, 189–207.CrossRefGoogle Scholar