  Learning from audio-visual data offers many possibilities to express
correspondence between the audio and visual content, similar to the human
perception that relates aural and visual information. In this work, we present
a method for self-supervised representation learning based on audio-visual
spatial alignment (AVSA), a more sophisticated alignment task than the
audio-visual correspondence (AVC). In addition to the correspondence, AVSA also
learns from the spatial location of acoustic and visual content. Based on
360$^\text{o}$ video and Ambisonics audio, we propose selection of visual
objects using object detection, and beamforming of the audio signal towards the
detected objects, attempting to learn the spatial alignment between objects and
the sound they produce. We investigate the use of spatial audio features to
represent the audio input, and different audio formats: Ambisonics, mono, and
stereo. Experimental results show a 10 $\%$ improvement on AVSA for the first
order ambisonics intensity vector (FOA-IV) in comparison with log-mel
spectrogram features; the addition of object-oriented crops also brings
significant performance increases for the human action recognition downstream
task. A number of audio-only downstream tasks are devised for testing the
effectiveness of the learnt audio feature representation, obtaining performance
comparable to state-of-the-art methods on acoustic scene classification from
ambisonic and binaural audio.
