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- W2031143922 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." @default.
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- W2031143922 date "2005-05-27" @default.
- W2031143922 modified "2023-10-03" @default.
- W2031143922 title "<title>Environmentally adaptive acoustic transmission loss prediction with neural networks</title>" @default.
- W2031143922 doi "https://doi.org/10.1117/12.606541" @default.
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