  Self-supervised audio-visual learning aims to capture useful representations
of video by leveraging correspondences between visual and audio inputs.
Existing approaches have focused primarily on matching semantic information
between the sensory streams. We propose a novel self-supervised task to
leverage an orthogonal principle: matching spatial information in the audio
stream to the positions of sound sources in the visual stream. Our approach is
simple yet effective. We train a model to determine whether the left and right
audio channels have been flipped, forcing it to reason about spatial
localization across the visual and audio streams. To train and evaluate our
method, we introduce a large-scale video dataset, YouTube-ASMR-300K, with
spatial audio comprising over 900 hours of footage. We demonstrate that
understanding spatial correspondence enables models to perform better on three
audio-visual tasks, achieving quantitative gains over supervised and
self-supervised baselines that do not leverage spatial audio cues. We also show
how to extend our self-supervised approach to 360 degree videos with ambisonic
audio.
