Self-Supervised Hierarchical Metrical Structure Modeling
This addresses the need for automated music analysis tools with reduced annotation effort, though it is incremental as it builds on existing self-supervised and metrical modeling approaches.
The paper tackles the problem of modeling hierarchical metrical structures in music without extensive labeled data, achieving performance comparable to supervised baselines on both symbolic and audio tasks.
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.