Spatial audio enhances immersion by reproducing 3D sound fields, with Ambisonics offering a scalable format for this purpose. While first-order Ambisonics (FOA) notably facilitates hardware-efficient acquisition and storage of sound fields as compared to high-order Ambisonics (HOA), its low spatial resolution limits realism, highlighting the need for Ambisonics upscaling (AU) as an approach for increasing the order of Ambisonics signals. In this work we propose DiffAU, a cascaded AU method that leverages recent developments in diffusion models combined with novel adaptation to spatial audio to generate 3rd order Ambisonics from FOA. By learning data distributions, DiffAU provides a principled approach that rapidly and reliably reproduces HOA in various settings. Experiments in anechoic conditions with multiple speakers, show strong objective and perceptual performance.

翻译：空间音频通过重现三维声场增强沉浸感，而Ambisonics为此提供了可扩展的格式。与高阶Ambisonics（HOA）相比，一阶Ambisonics（FOA）虽显著便于高效采集与存储声场，但其低空间分辨率限制了真实感，凸显了将Ambisonics升阶（AU）作为增加Ambisonics信号阶数方法的必要性。本文提出DiffAU，一种融合扩散模型最新进展与空间音频新型适配方法的级联AU技术，可从FOA生成三阶Ambisonics。通过学习数据分布，DiffAU提供了一种能在多种场景下快速可靠重现HOA的规范化方法。在多扬声器消声环境下的实验表明，该方法在客观指标与感知效果上均表现优异。