  Existing machine learning research has achieved promising results in monaural
audio-visual separation (MAVS). However, most MAVS methods purely consider what
the sound source is, not where it is located. This can be a problem in VR/AR
scenarios, where listeners need to be able to distinguish between similar audio
sources located in different directions. To address this limitation, we have
generalized MAVS to spatial audio separation and proposed LAVSS: a
location-guided audio-visual spatial audio separator. LAVSS is inspired by the
correlation between spatial audio and visual location. We introduce the phase
difference carried by binaural audio as spatial cues, and we utilize positional
representations of sounding objects as additional modality guidance. We also
leverage multi-level cross-modal attention to perform visual-positional
collaboration with audio features. In addition, we adopt a pre-trained monaural
separator to transfer knowledge from rich mono sounds to boost spatial audio
separation. This exploits the correlation between monaural and binaural
channels. Experiments on the FAIR-Play dataset demonstrate the superiority of
the proposed LAVSS over existing benchmarks of audio-visual separation. Our
project page: https://yyx666660.github.io/LAVSS/.
