Masked diffusion enables coherent beat tracking
Current neural networks for beat tracking generate invalid outputs, such as consecutive downbeats and erratic tempo changes, even when these are not present in the training data. Heavy post-processing techniques can alleviate these problems, but the original cause of this inconsistent behaviour remains unknown. We hypothesise that it stems from inadequate modelling of multiple plausible output beat grids, resulting in an invalid mixture of competing interpretations. We propose a masked diffusion approach that properly models multiple outputs and enables the model to build coherent predictions through iterative inference. We devise three modifications to standard masked diffusion that enab...
| Authors | Francesco Foscarin, Filip Korzeniowski, Richard Vogl |
|---|---|
| Primary category | cs.SD |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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