Environmentally adaptive acoustic transmission loss prediction in turbulent and nonturbulent atmospheres

An environmentally adaptive system for prediction of acoustic transmission loss (TL) in the atmosphere is developed in this paper. This system uses several back propagation neural network predictors, each corresponding to a specific environmental condition. The outputs of the expert predictors are c...

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Bibliographic Details
Published in:Neural networks 2007-05, Vol.20 (4), p.484-497
Main Authors: Wichern, Gordon, Azimi-Sadjadi, Mahmood R., Mungiole, Michael
Format: Article
Language:English
Subjects:
Acoustics
Atmospheric acoustics
Environment
Fuzzy Logic
Fuzzy-logic fusion
Neural Networks (Computer)
Parabolic equation
Predictive Value of Tests
Signal Processing, Computer-Assisted
Turbulent scattering
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Summary:An environmentally adaptive system for prediction of acoustic transmission loss (TL) in the atmosphere is developed in this paper. This system uses several back propagation neural network predictors, each corresponding to a specific environmental condition. The outputs of the expert predictors are combined using a fuzzy confidence measure and a nonlinear fusion system. Using this prediction methodology the computational intractability of traditional acoustic model-based approaches is eliminated. The proposed TL prediction system is tested on two synthetic acoustic data sets for a wide range of geometrical, source and environmental conditions including both nonturbulent and turbulent atmospheres. Test results of the system showed root mean square (RMS) errors of 1.84 dB for the nonturbulent and 1.36 dB for the turbulent conditions, respectively, which are acceptable levels for near real-time performance. Additionally, the environmentally adaptive system demonstrated improved TL prediction accuracy at high frequencies and large values of horizontal separation between source and receiver.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2007.04.025