Neural fields have successfully been used in many research fields for their native ability to estimate a continuous function from a finite number of observations. In audio processing, this technique has been applied to acoustic and head-related transfer function interpolation. However, most of the existing methods estimate the real-valued magnitude function over a predefined discrete set of frequencies. In this study, we propose a novel approach for steering vector interpolation that regards frequencies as continuous input variables. Moreover, we propose a novel unsupervised regularization term enforcing the estimated filters to be causal. The experiment using real steering vectors show that the proposed frequency resolution-free method outperformed the existing methods operating over discrete set of frequencies.

翻译：神经场凭借其从有限观测中估计连续函数的天然能力，已在众多研究领域成功应用。在音频处理中，该技术已被用于声学传递函数和头相关传递函数的插值。然而，现有方法大多在预定义的离散频率集上估计实值幅度函数。本研究提出一种新型导向矢量插值方法，将频率视为连续输入变量。此外，我们提出一种新型无监督正则化项，确保估计的滤波器具有因果性。基于真实导向矢量的实验表明，所提出的无频率分辨率方法在性能上优于现有基于离散频率集的方法。