# Controlling Contrastive Self-Supervised Learning with Knowledge-Driven Multiple Hypothesis: Application to Beat Tracking

arXiv: https://arxiv.org/abs/2510.25560 · HTML: https://mlsanity.com/paper/2510.25560/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2025.

## Built on by (most cited first, 2 of 2)

- [Masked diffusion enables coherent beat tracking](https://mlsanity.com/paper/2608.04624/) (2026)
- [Explore This! Beat and Downbeat Tracking From a Learned Tatum Grid](https://mlsanity.com/paper/2610.14817/) (2026)

## Abstract (arXiv)

Ambiguities in data and problem constraints can lead to diverse, equally plausible outcomes for a machine learning task. In beat and downbeat tracking, for instance, different listeners may adopt various rhythmic interpretations, none of which would necessarily be incorrect. To address this, we propose a contrastive self-supervised pre-training approach that leverages multiple hypotheses about possible positive samples in the data. Our model is trained to learn representations compatible with different such hypotheses, which are selected with a knowledge-based scoring function to retain the most plausible ones. When fine-tuned on labeled data, our model outperforms existing methods on standard benchmarks, showcasing the advantages of integrating domain knowledge with multi-hypothesis selection in music representation learning in particular.