AI Medical Compendium Journal:
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Showing 11 to 16 of 16 articles

Data driven source localization using a library of nearby shipping sources of opportunity.

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A library of broadband (100-1000 Hz) channel impulse responses (CIRs) estimated between a short bottom-mounted vertical line array (VLA) in the Santa Barbara channel and selected locations along the tracks of 27 isolated transiting ships, cumulated o...

Physics-informed neural networks for one-dimensional sound field predictions with parameterized sources and impedance boundaries.

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Realistic sound is essential in virtual environments, such as computer games and mixed reality. Efficient and accurate numerical methods for pre-calculating acoustics have been developed over the last decade; however, pre-calculating acoustics makes ...

Validating two geospatial models of continental-scale environmental sound levels.

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Modeling outdoor environmental sound levels is a challenging problem. This paper reports on a validation study of two continental-scale machine learning models using geospatial layers as inputs and the summer daytime A-weighted L as a validation metr...

Correspondence between three-dimensional ear depth information derived from two-dimensional images and magnetic resonance imaging: Use of a neural-network model.

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There is much interest in anthropometric-derived head-related transfer functions (HRTFs) for simulating audio for virtual-reality systems. Three-dimensional (3D) anthropometric measures can be measured directly from individuals, or indirectly simulat...

Underwater acoustic target recognition using attention-based deep neural network.

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Underwater acoustic target recognition based on ship-radiated noise is difficult owing to the complex marine environment and the interference by multiple targets. As an important technology for target recognition, deep-learning has high accuracy but ...

Validating deep learning seabed classification via acoustic similarity.

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While seabed characterization methods have often focused on estimating individual sediment parameters, deep learning suggests a class-based approach focusing on the overall acoustic effect. A deep learning classifier-trained on 1D synthetic waveforms...