We present SPEAR, a continuous receiver-to-receiver acoustic neural warping field for spatial acoustic effects prediction in an acoustic 3D space with a single stationary audio source. Unlike traditional source-to-receiver modelling methods that require prior space acoustic properties knowledge to rigorously model audio propagation from source to receiver, we propose to predict by warping the spatial acoustic effects from one reference receiver position to another target receiver position, so that the warped audio essentially accommodates all spatial acoustic effects belonging to the target position. SPEAR can be trained in a data much more readily accessible manner, in which we simply ask two robots to independently record spatial audio at different positions. We further theoretically prove the universal existence of the warping field if and only if one audio source presents. Three physical principles are incorporated to guide SPEAR network design, leading to the learned warping field physically meaningful. We demonstrate SPEAR superiority on both synthetic, photo-realistic and real-world dataset, showing the huge potential of SPEAR to various down-stream robotic tasks.

翻译：本文提出SPEAR——一种用于单稳态声源三维声学空间中空间声效预测的连续接收端到接收端声学神经形变场。传统声源-接收端建模方法需要先验空间声学特性知识来严格建模从声源到接收端的音频传播，与之不同，我们通过将空间声效从一个参考接收端位置形变至另一个目标接收端位置进行预测，使得形变后的音频本质上包含了目标位置的所有空间声学效应。SPEAR可采用更易获取数据的方式进行训练：仅需两个机器人在不同位置独立录制空间音频。我们进一步从理论上证明了当且仅当存在单一声源时形变场具有普适存在性。通过融入三大物理原理指导SPEAR网络设计，使学习到的形变场具有明确的物理意义。我们在合成数据集、逼真渲染数据集及真实场景数据集上验证了SPEAR的优越性，展现了其在各类下游机器人任务中的巨大潜力。