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We introduce an approach to convert mono audio recorded by a 360 video camerainto spatial audio, a representation of the distribution of sound over the fullviewing sphere. Spatial audio is an important component of immersive 360 videoviewing, but spatial audio microphones are still rare in current 360 videoproduction. Our system consists of end-to-end trainable neural networks thatseparate individual sound sources and localize them on the viewing sphere,conditioned on multi-modal analysis of audio and 360 video frames. We introduceseveral datasets, including one filmed ourselves, and one collected in-the-wildfrom YouTube, consisting of 360 videos uploaded with spatial audio. Duringtraining, ground-truth spatial audio serves as self-supervision and a mixeddown mono track forms the input to our network. Using our approach, we showthat it is possible to infer the spatial location of sound sources based onlyon 360 video and a mono audio track.

Self-Supervised Generation of Spatial Audio for 360 Video