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Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields
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Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields
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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.
Forward citations
Cited by 1 Pith paper
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Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation
Ray-casting local geometry features from a rough mesh into a neural acoustic field improves room impulse response prediction, especially with little training data.
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