用方向感知神经场建模声场，实现少样本高保真混响插值。
Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses
- 基于全向声学场的神经场模型，显式编码声音方向信息
- 在少样本条件下实现高质量混响插值，支持新场景快速适应
- 适合需要精准声场重建的虚拟现实与音频渲染应用
声场特性与声源和听者周围环境的几何及空间属性密切相关。声波传播的物理规律体现在时域信号中，即房间冲激响应（RIR）。以往基于神经场（NF）的方法可从有限的RIR测量中学习空间连续的RIR表示，但主要关注单声道全向或双耳听觉，未能精确捕捉单点处真实声场的方向特性。本文提出方向感知神经场（DANF），通过采用Ambisonic格式的RIR更显式地融入方向信息。DANF天然捕捉声源与听者之间的空间关系，进一步设计方向感知损失函数。此外，我们研究了DANF在多种方式下对新房间的适应能力，包括低秩适配。
原文摘要 · Abstract (English)
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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