Self-Supervised Hierarchical Metrical Structure Modeling

• URL: http://arxiv.org/abs/2210.17183v1
• Date: Mon, 31 Oct 2022 10:05:19 GMT
• Title: Self-Supervised Hierarchical Metrical Structure Modeling
• Authors: Junyan Jiang and Gus Xia
• Abstract summary: We propose a novel method to model hierarchical metrical structures for both symbolic music and audio signals. 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. All demos, source code and pre-trained models are publicly available on GitHub.
• Score: 3.167685495996986
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 procedural 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.

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