Audio Barlow Twins: Self-Supervised Audio Representation Learning

Jonah Anton; Harry Coppock; Pancham Shukla; Björn W. Schuller

ICASSP 2023accepted0 citations

Audio Barlow Twins: Self-Supervised Audio Representation Learning

Jonah Anton, Harry Coppock, Pancham Shukla, Björn W. Schuller

Abstract

The Barlow Twins self-supervised learning objective requires neither negative samples or asymmetric learning updates, achieving results on a par with the current state-of-the-art within Computer Vision. As such, we present Audio Barlow Twins, a novel self-supervised audio representation learning approach, adapting Barlow Twins to the audio domain. We pre-train on the large-scale audio dataset AudioSet, and evaluate the quality of the learnt representations on 18 tasks from the HEAR 2021 Challenge, achieving results which outperform, or otherwise are on a par with, the current state-of-the-art for instance discrimination self-supervised learning approaches to audio representation learning. Code at https://github.com/jonahanton/SSL_audio.

BibTeX

@inproceedings{icassp2023_audiobarlowtwins,
  title = {Audio Barlow Twins: Self-Supervised Audio Representation Learning},
  author = {Jonah Anton and Harry Coppock and Pancham Shukla and Björn W. Schuller},
  booktitle = {ICASSP 2023},
  year = {2023}
}