Skip to main content
×
Home
    • Aa
    • Aa

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

  • E.D. Chesmore (a1) and E. Ohya (a2)
Abstract
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.

Copyright
Corresponding author
*Fax: 01904 432335 E-mail: edc1@ohm.york.ac.uk
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

S.E. Anderson , A.S. Dave & D. Margoliash (1996) Template-based automatic recognition of birdsong syllables from continuous recordings. Journal of the Acoustical Society of America 100, 12091217.

E.D. Chesmore (2001) Application of time domain signal coding and artificial neural networks to passive acoustical identification of animals. Applied Acoustics 62, 13591374.

D.W. Hagstrum , K.W. Vick & J.C. Webb (1990) Acoustic monitoring of Rhizopertha dominica (Coleoptera: Bostrichidae) populations in stored wheat. Journal of Economic Entomology 83, 625628.

A. MacLeod , H.F. Evans , R.H.A. Baker (2002) An analysis of pest risk from an Asian longhorn beetle (Anoplophora glabripennis) to hardwood trees in the European community. Crop Protection 21, 635645.

R.W. Mankin , J. Brandhurst-Hubbard , K.L. Flanders , M. Zhang , R.L. Crocker , S.L. Lapointe , C.W. McCoy , J.R. Fisher & D.K. Weaver (2000) Eavesdropping on insects hidden in soil and interior structures of plants. Journal of Economic Entomology 93, 11731182.

H. Mills (1995) Automatic detection and classification of nocturnal migrant bird calls. Journal of the Acoustical Society of America 97, 33703371.

S.O. Murray , E. Mercado & H.L. Roitblat (1998) The neural network classification of false killer whale (Pseudorca crassidens) vocalizations. Journal of the Acoustical Society of America 104, 36263633.

S. Parsons (2001) Identification of New Zealand bats in flight from analysis of echolocation calls by artificial neural networks. Journal of Zoology 253, 447456.

D. Reby , S. Lek , I. Dimopoulos , J. Joachim , J. Lauga & S. Aulagnier (1997) Artificial neural networks as a classification method in the behavioural sciences. Behavioural Processes 40, 3543.

F. Schwenker , C. Dietrich , H.A. Kestler , K. Riede & G. Palm (2003) Radial basis function neural networks and temporal fusion for the classification of bioacoustic time series. Neurocomputing 51, 265275.

D. Shuman , J.A. Coffelt , K.W. Vick & R.W. Mankin (1993) Quantitative acoustical detection of larvae feeding inside kernels of grain. Journal of Economic Entomology 86, 933938.

D. Shuman , D.K. Weaver & R.W. Mankin (1997) Quantifying larval infestation with an acoustical sensor array and cluster analysis of cross-correlation outputs. Applied Acoustics 50, 279296.

A.M.R. Terry , P.K. McGregor (2002) Census and monitoring based on individually identifiable vocalizations: the role of neural networks. Animal Conservation 5, 103111.

N. Vaughan , G. Jones & S. Harris (1996) Identification of British bat species by multivariate analysis of echolocation call parameters. Bioacoustics 7, 189207.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Bulletin of Entomological Research
  • ISSN: 0007-4853
  • EISSN: 1475-2670
  • URL: /core/journals/bulletin-of-entomological-research
Please enter your name
Please enter a valid email address
Who would you like to send this to? *
×

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 17 *
Loading metrics...

Abstract views

Total abstract views: 95 *
Loading metrics...

* Views captured on Cambridge Core between September 2016 - 29th May 2017. This data will be updated every 24 hours.