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Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses

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arxiv 2505.13617 v1 pith:45PE5SIP submitted 2025-05-19 eess.AS cs.AIcs.CVcs.LGcs.SD

Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses

classification eess.AS cs.AIcs.CVcs.LGcs.SD
keywords sounddanfdirection-awarefieldneuralcharacteristicsdirectionalfields
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The characteristics of a sound field are intrinsically linked to the geometric and spatial properties of the environment surrounding a sound source and a listener. The physics of sound propagation is captured in a time-domain signal known as a room impulse response (RIR). Prior work using neural fields (NFs) has allowed learning spatially-continuous representations of RIRs from finite RIR measurements. However, previous NF-based methods have focused on monaural omnidirectional or at most binaural listeners, which does not precisely capture the directional characteristics of a real sound field at a single point. We propose a direction-aware neural field (DANF) that more explicitly incorporates the directional information by Ambisonic-format RIRs. While DANF inherently captures spatial relations between sources and listeners, we further propose a direction-aware loss. In addition, we investigate the ability of DANF to adapt to new rooms in various ways including low-rank adaptation.

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Cited by 1 Pith paper

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  1. EigeNet: Geometry-Informed Multi-Modal Learning for Few-shot Novel View RIR Prediction

    cs.SD 2026-05 unverdicted novelty 5.0

    EigeNet applies a cross-view alternate-attention transformer with geometry modulation for few-shot novel-view RIR prediction, reporting SOTA results on simulated and real data.