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- W2769360526 abstract "Due to time constraints imposed by operational considerations, reverberation models used by the Navy split the computational work by modeling the propagation and the scattering separately. The scattering module is typically a “look-up” table linking the incident sound field at any point on the boundary to a pre-computed statistic of the scattering. There are at least two significant drawbacks to this standard modeling approach. First, each acoustic interaction with the boundary generates a unique realization of a stochastic process, and consequently, no single statistic of the scattering always can be justified and the correct modeling of the statistics of reverberation time series must acknowledge this event-to-event variation. Second, pre-computing the scattering based on an assumed statistical description of the unresolved boundary irregularities without consideration of the resolved boundary irregularities prohibits the coherent inclusion in that scattering calculation of the effects that the resolved boundary irregularities have on the scattering. Machine-learned models for the boundary scattering hold the promise of addressing both of these issues. The purpose of this talk is to explore these issues and the prospect of developing a machine-learned model for the acoustic impulse response of a multi-scale rough ocean boundary. [This work was supported by the U.S. Office of Naval Research.]" @default.
- W2769360526 created "2017-12-04" @default.
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- W2769360526 date "2017-10-01" @default.
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- W2769360526 title "Machine learning the acoustic impulse response of a rough surface" @default.
- W2769360526 doi "https://doi.org/10.1121/1.5014114" @default.
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