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Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields

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arxiv 2309.15977 v1 pith:MWNZ733V submitted 2023-09-27 cs.SD cs.CVeess.AS

Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields

classification cs.SD cs.CVeess.AS
keywords acousticneuralaudiofieldcontextenergyenvironmentimpulse
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Room impulse response (RIR), which measures the sound propagation within an environment, is critical for synthesizing high-fidelity audio for a given environment. Some prior work has proposed representing RIR as a neural field function of the sound emitter and receiver positions. However, these methods do not sufficiently consider the acoustic properties of an audio scene, leading to unsatisfactory performance. This letter proposes a novel Neural Acoustic Context Field approach, called NACF, to parameterize an audio scene by leveraging multiple acoustic contexts, such as geometry, material property, and spatial information. Driven by the unique properties of RIR, i.e., temporal un-smoothness and monotonic energy attenuation, we design a temporal correlation module and multi-scale energy decay criterion. Experimental results show that NACF outperforms existing field-based methods by a notable margin. Please visit our project page for more qualitative results.

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Cited by 1 Pith paper

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

  1. Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation

    cs.SD 2025-09 conditional novelty 6.0

    Ray-casting local geometry features from a rough mesh into a neural acoustic field improves room impulse response prediction, especially with little training data.