  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.
