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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
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
Corresponding
E-mail address:

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.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2004

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