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Proceedings Paper

Automatic frog call monitoring system: a machine learning approach
Author(s): Gary G. Yen; Qiang Fu
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Paper Abstract

Automatic recognition of frog vocalization is considered a valuable tool for a variety of biological research and environmental monitoring applications. In this research an automatic monitoring system, which can recognize the vocalizations of four species of frogs and can identify different individuals within the species of interest, is proposed. For the desired monitoring system, species identification is performed first with the proposed filtering and grouping algorithm. Individual identification, which can estimate frog population within the specific species, is performed in the second stage. Digital signal pre-processing, feature extraction, dimensionality reduction, and neural network pattern classification are performed step by step in this stage. Wavelet Packet feature extraction together with two different dimension reduction algorithms are synergistically integrated to produce final feature vectors, which are to be fed into a neural network classifier. The simulation results show the promising future of deploying an array of continuous, on-line environmental monitoring systems based upon nonintrusive analysis of animal calls.

Paper Details

Date Published: 11 March 2002
PDF: 12 pages
Proc. SPIE 4739, Applications and Science of Computational Intelligence V, (11 March 2002); doi: 10.1117/12.458716
Show Author Affiliations
Gary G. Yen, Oklahoma State Univ. (United States)
Qiang Fu, Oklahoma State Univ. (United States)


Published in SPIE Proceedings Vol. 4739:
Applications and Science of Computational Intelligence V
Kevin L. Priddy; Paul E. Keller; Peter J. Angeline, Editor(s)

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