We apply our self-supervised audio-visual representation to sound localization, action recognition, and on/off-screen audio-visual source separation.
The thud of a bouncing ball, the onset of speech as lips open — when visual and audio events occur together, it suggests that there might be a common, underlying event that produced both signals.
In this paper, we argue that the visual and audio components of a video signal should be modeled jointly using a fused multisensory representation.We propose to learn such a representation in a self-supervised way, by training a neural network to predict whether video frames and audio are temporally aligned. We use this learned representation for three applications: (a)
sound source localization, i.e. visualizing the source of sound in a video;
(b) audio-visual action recognition; and (c) on/off-screen audio source separation, e.g.
removing the off-screen translator's voice from a foreign official's speech.