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Proceedings Paper

Environmentally adaptive acoustic transmission loss prediction with neural networks
Author(s): Gordon Wichern; Mahmood R. Azimi-Sadjadi; Michael Mungiole
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Paper Abstract

Prediction of acoustic transmission loss (TL), or the attenuation of sound pressure level (SPL) is a complex problem dependent on a variety of physical parameters. Prediction of the TL using a numeric parabolic equation (PE) method is often accepted as a method of providing accurate TL prediction, but the large computational time is a hinderance in applications requiring real-time situation awareness. In order to overcome these extreme computational requirements a neural network-based environmentally adaptive TL prediction method is proposed and developed in this paper. This method uses multiple back-propagation neural network (BPNN) predictors, each trained on specific environmental conditions, and then probabilistically combines the outputs of these predictors in a fusion center to obtain a final TL estimate. This method is implemented on a data set generated using a PE model for a wide range of geometric and environmental parameters. The results are then benchmarked against a single neural network-based prediction scheme.

Paper Details

Date Published: 27 May 2005
PDF: 10 pages
Proc. SPIE 5796, Unattended Ground Sensor Technologies and Applications VII, (27 May 2005); doi: 10.1117/12.606541
Show Author Affiliations
Gordon Wichern, Colorado State Univ. (United States)
Mahmood R. Azimi-Sadjadi, Colorado State Univ. (United States)
Michael Mungiole, Army Research Lab. (United States)


Published in SPIE Proceedings Vol. 5796:
Unattended Ground Sensor Technologies and Applications VII
Edward M. Carapezza, Editor(s)

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