Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

classification eess.AScs.AIcs.CVcs.LGcs.SD
keywords sounddanfdirection-awarefieldneuralcharacteristicsdirectionalfields
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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

    cs.SD 2026-05 unverdicted novelty 5.0 of 10

    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.

Pith tools