A source-position-dependent implicit neural representation for the directional weighting in a physics-constrained kernel enables sound field estimation that generalizes to unseen source positions without per-snapshot fine-tuning.
It can be applied to a wide variety of downstream tasks, for example, acoustic imaging [1], room acous- tic analysis [2], and spatial audio reproduction and control [3, 4]
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Learning-based Physics-Constrained Neural Kernel for Sound Field Estimation With Source-Position-Dependent Directional Weighting
A source-position-dependent implicit neural representation for the directional weighting in a physics-constrained kernel enables sound field estimation that generalizes to unseen source positions without per-snapshot fine-tuning.