Self-Supervised Hierarchical Metrical Structure Modeling

Document Type

Conference Proceeding

Publication Title

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings


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 GitHub1



Publication Date



hierarchical structure analysis, Metrical structure, music understanding, self-supervised learning

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