  We propose a novel method to model hierarchical metrical structures for both
symbolic music and audio signals in a self-supervised manner with minimal
domain knowledge. The model trains and inferences on beat-aligned music signals
and predicts an 8-layer hierarchical metrical tree from beat, measure to the
section level. The training procedure does not require any hierarchical
metrical labeling except for beats, purely relying on the nature of metrical
regularity and inter-voice consistency as inductive biases. We show in
experiments that the method achieves comparable performance with supervised
baselines on multiple metrical structure analysis tasks on both symbolic music
and audio signals. All demos, source code and pre-trained models are publicly
available on GitHub.
