{"ID":2868817,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.15946","arxiv_id":"2509.15946","title":"Differentiable Acoustic Radiance Transfer","abstract":"Geometric acoustics is an efficient framework for room acoustics modeling, governed by the canonical time-dependent rendering equation. Acoustic radiance transfer (ART) solves the equation by discretization, modeling time- and direction-dependent energy exchange between surface patches with flexible material properties. We introduce DART, an efficient, differentiable implementation of ART that enables gradient-based optimization of material properties. We evaluate DART on a simpler variant of acoustic field learning that aims to predict energy responses for novel source-receiver configurations. Experimental results demonstrate that DART generalizes better under sparse measurement scenarios than existing signal processing and neural network baselines, while maintaining simplicity and full interpretability. We open-source our implementation.","short_abstract":"Geometric acoustics is an efficient framework for room acoustics modeling, governed by the canonical time-dependent rendering equation. Acoustic radiance transfer (ART) solves the equation by discretization, modeling time- and direction-dependent energy exchange between surface patches with flexible material properties...","url_abs":"https://arxiv.org/abs/2509.15946","url_pdf":"https://arxiv.org/pdf/2509.15946v2","authors":"[\"Sungho Lee\",\"Matteo Scerbo\",\"Seungu Han\",\"Min Jun Choi\",\"Kyogu Lee\",\"Enzo De Sena\"]","published":"2025-09-19T12:54:51Z","proceeding":"cs.SD","tasks":"[\"cs.SD\",\"eess.AS\",\"eess.SP\"]","methods":"[]","has_code":false}