# Learning Hierarchical Metrical Structure Beyond Measures

arXiv: https://arxiv.org/abs/2209.10259 · HTML: https://mlsanity.com/paper/2209.10259/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 51 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [POP909: A Pop-song Dataset for Music Arrangement Generation](https://mlsanity.com/paper/2008.07142/) (2020)
- [The challenge of realistic music generation: modelling raw audio at scale](https://mlsanity.com/paper/1806.10474/) (2018)
- [Automatic Analysis and Influence of Hierarchical Structure on Melody, Rhythm and Harmony in Popular Music](https://mlsanity.com/paper/2010.07518/) (2020)
- [Unsupervised Learning of Deep Features for Music Segmentation](https://mlsanity.com/paper/2108.12955/) (2021)
- [Imposing higher-level Structure in Polyphonic Music Generation using Convolutional Restricted Boltzmann Machines and Constraints](https://mlsanity.com/paper/1612.04742/) (2016)
- [Robust Downbeat Tracking Using an Ensemble of Convolutional Networks](https://mlsanity.com/paper/1605.08396/) (2016)
- [Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations](https://mlsanity.com/paper/1706.09552/) (2017)
- [Enhanced Hierarchical Music Structure Annotations via Feature Level Similarity Fusion](https://mlsanity.com/paper/1902.01023/) (2019)

## Abstract (arXiv)

Music contains hierarchical structures beyond beats and measures. While hierarchical structure annotations are helpful for music information retrieval and computer musicology, such annotations are scarce in current digital music databases. In this paper, we explore a data-driven approach to automatically extract hierarchical metrical structures from scores. We propose a new model with a Temporal Convolutional Network-Conditional Random Field (TCN-CRF) architecture. Given a symbolic music score, our model takes in an arbitrary number of voices in a beat-quantized form, and predicts a 4-level hierarchical metrical structure from downbeat-level to section-level. We also annotate a dataset using RWC-POP MIDI files to facilitate training and evaluation. We show by experiments that the proposed method performs better than the rule-based approach under different orchestration settings. We also perform some simple musicological analysis on the model predictions. All demos, datasets and pre-trained models are publicly available on Github.