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

Christopher G. Lowrie; Fredric M. Ham

doi:10.1117/12.661513

17 April 2006 Classification of infrasound events using hermite polynomial preprocessing and radial basis function neural networks

Christopher G. Lowrie, Fredric M. Ham

Author Affiliations +

Proceedings Volume 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV; 624715 (2006) https://doi.org/10.1117/12.661513
Event: Defense and Security Symposium, 2006, Orlando (Kissimmee), Florida, United States

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.

Citation Download Citation

Christopher G. Lowrie and Fredric M. Ham "Classification of infrasound events using hermite polynomial preprocessing and radial basis function neural networks", Proc. SPIE 6247, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV, 624715 (17 April 2006); https://doi.org/10.1117/12.661513

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