  Audio-visual learning, aimed at exploiting the relationship between audio and
visual modalities, has drawn considerable attention since deep learning started
to be used successfully. Researchers tend to leverage these two modalities
either to improve the performance of previously considered single-modality
tasks or to address new challenging problems. In this paper, we provide a
comprehensive survey of recent audio-visual learning development. We divide the
current audio-visual learning tasks into four different subfields: audio-visual
separation and localization, audio-visual correspondence learning, audio-visual
generation, and audio-visual representation learning. State-of-the-art methods
as well as the remaining challenges of each subfield are further discussed.
Finally, we summarize the commonly used datasets and performance metrics.
