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.

翻译：声场的特性本质上与声源和听者周围环境的几何及空间属性相关联。声传播的物理特性被捕获在一个称为房间脉冲响应（RIR）的时域信号中。先前利用神经场（NFs）的研究使得能够从有限的RIR测量中学习RIR的空间连续表示。然而，以往的基于NF的方法主要关注单声道全向或至多双声道听者，这未能精确捕捉单点处真实声场的定向特性。我们提出了一种方向感知神经场（DANF），它通过Ambisonic格式的RIR更明确地整合了方向信息。尽管DANF本质上捕获了声源与听者之间的空间关系，我们进一步提出了一种方向感知损失。此外，我们研究了DANF以多种方式（包括低秩自适应）适应新房间的能力。