Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment

TL;DR

This paper introduces AVSA, a self-supervised learning method that leverages spatial alignment between audio and visual data in 360° videos to improve audio representation and downstream task performance.

Contribution

The work proposes a novel AVSA task that incorporates spatial information and object detection to enhance audio-visual representation learning from 360° videos.

Findings

10% improvement with FOA-IV features over log-mel spectrograms

Object-oriented crops boost human action recognition accuracy

Achieves state-of-the-art results on acoustic scene classification

Abstract

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.