Explore This! Beat and Downbeat Tracking From a Learned Tatum Grid
State-of-the-art beat tracking is based on neural networks that model beats and downbeats as individual events, often followed by a separate Dynamic Bayesian Network (DBN) enforcing musical consistency. We propose a novel architecture that combines deep learning with differentiable theory-based modules: Specifically, we design a system that first predicts a tatum grid, then groups tatum events to beats and downbeats, explicitly modelling multiple options for the current tempo and time signature in a bar-pointer-like fashion. Our approach improves continuity metrics compared to existing DBN-free systems and is more versatile than the de-facto standard DBN, but still falls behind the best D...
| Authors | Robert Kihlborg, André Holzapfel, Jan Schlüter |
|---|---|
| Primary category | cs.SD |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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