Audio Visual Scene Analysis with Self Supervised Multisensory Features
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. Code, models, and video results are available on our webpage: this http URL
ECCV