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.
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提出了一种新颖的神经音频环境场方法（NACF），通过利用多个声学环境上下文参数化音频场景，考虑了声学属性，实验结果表明 NACF 优于现有基于场的方法。