mlsanityData to 7 Oct 2026

Audio Barlow Twins: Self-Supervised Audio Representation Learning

arXiv 2209.14345 ↗First posted 28 Sep 2022cs.SD cs.LG eess.AScited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 32 of its 37 references that are in the map. First posted on arXiv 28 Sep 2022.

Builds on

32 of 37 references.

References · 32

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20 more
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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 3.

4 papers.

  1. 2025PESTO: Real-Time Pitch Estimation with Self-supervised Transposition-equivariant Objectivecited by 3
  2. 2023Understanding Self-Supervised Learning of Speech Representation via Invariance and Redundancy Reductioncited by 0
  3. 2025Enhancing User Sequence Modeling through Barlow Twins-based Self-Supervised Learningcited by 0
  4. 2025Unify Variables in Neural Scaling Laws for General Audio Representations via Embedding Effective Rankcited by 0