# S-KEY: Self-supervised Learning of Major and Minor Keys from Audio

arXiv: https://arxiv.org/abs/2501.12907 · HTML: https://mlsanity.com/paper/2501.12907/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2025.

## Built on by (most cited first, 4 of 4)

- [Emergent musical properties of a transformer under contrastive self-supervised learning](https://mlsanity.com/paper/2506.23873/) (2025)
- [Translation-Equivariant Self-Supervised Learning for Pitch Estimation with Optimal Transport](https://mlsanity.com/paper/2508.01493/) (2025)
- [CoJEPA: Combining Contrastive Learning and JEPA for Global-Local Music Representations](https://mlsanity.com/paper/2608.30974/) (2026)
- [From Isolated Feature to Orbits: Discovering Music Concepts via Multi-SAE Alignment](https://mlsanity.com/paper/2610.01864/) (2026)

## Abstract (arXiv)

STONE, the current method in self-supervised learning for tonality estimation in music signals, cannot distinguish relative keys, such as C major versus A minor. In this article, we extend the neural network architecture and learning objective of STONE to perform self-supervised learning of major and minor keys (S-KEY). Our main contribution is an auxiliary pretext task to STONE, formulated using transposition-invariant chroma features as a source of pseudo-labels. S-KEY matches the supervised state of the art in tonality estimation on FMAKv2 and GTZAN datasets while requiring no human annotation and having the same parameter budget as STONE. We build upon this result and expand the training set of S-KEY to a million songs, thus showing the potential of large-scale self-supervised learning in music information retrieval.