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Proceedings Paper

Classification of infrasound events using hermite polynomial preprocessing and radial basis function neural networks
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Paper Abstract

A method of infrasonic signal classification using hermite polynomials for signal preprocessing is presented. Infrasound is a low frequency acoustic phenomenon typically in the frequency range 0.01 Hz to 10 Hz. Data collected from infrasound sensors are preprocessed using a hermite orthogonal basis inner product approach. The hermite preprocessed signals result in feature vectors that are used as input to a parallel bank of radial basis function neural networks (RBFNN) for classification. The spread and threshold values for each of the RBFNN are then optimized. Robustness of this classification method is tested by introducing unknown events outside the training set and counting errors. The hermite preprocessing method is shown to have superior performance compared to a standard cepstral preprocessing method.

Paper Details

Date Published: 17 April 2006
PDF: 7 pages
Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 624715 (17 April 2006); doi: 10.1117/12.661513
Show Author Affiliations
Christopher G. Lowrie, Florida Institute of Technology (United States)
Fredric M. Ham, Florida Institute of Technology (United States)


Published in SPIE Proceedings Vol. 6247:
Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Harold H. Szu, Editor(s)

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