# Cadence Detection in Symbolic Classical Music using Graph Neural Networks

arXiv: https://arxiv.org/abs/2208.14819 · HTML: https://mlsanity.com/paper/2208.14819/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 31 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Benchmarking Graph Neural Networks](https://mlsanity.com/paper/2003.00982/) (2020)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks](https://mlsanity.com/paper/2103.08826/) (2021)
- [A Survey of Heterogeneous Information Network Analysis](https://mlsanity.com/paper/1511.04854/) (2015)
- [Partitura: A Python Package for Symbolic Music Processing](https://mlsanity.com/paper/2206.01071/) (2022)
- [Artist Similarity with Graph Neural Networks](https://mlsanity.com/paper/2107.14541/) (2021)
- [Beneath (or beyond) the surface: Discovering voice-leading patterns with skip-grams](https://mlsanity.com/paper/2006.15399/) (2020)

## Built on by (most cited first, 11 of 11)

- [Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features](https://mlsanity.com/paper/2307.03544/) (2023)
- [Musical Voice Separation as Link Prediction: Modeling a Musical Perception Task as a Multi-Trajectory Tracking Problem](https://mlsanity.com/paper/2304.14848/) (2023)
- [GraphMuse: A Library for Symbolic Music Graph Processing](https://mlsanity.com/paper/2407.12671/) (2024)
- [Symbolic Music Structure Analysis with Graph Representations and Changepoint Detection Methods](https://mlsanity.com/paper/2303.13881/) (2023)
- [BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps](https://mlsanity.com/paper/2604.19532/) (2026)
- [Graph-based Polyphonic Multitrack Music Generation](https://mlsanity.com/paper/2307.14928/) (2023)
- [AnalysisGNN: Unified Music Analysis with Graph Neural Networks](https://mlsanity.com/paper/2509.06654/) (2025)
- [Perception-Inspired Graph Convolution for Music Understanding Tasks](https://mlsanity.com/paper/2405.09224/) (2024)
- [Combinatorial music generation model with song structure graph analysis](https://mlsanity.com/paper/2312.15400/) (2023)
- [MUSE-Explainer: Counterfactual Explanations for Symbolic Music Graph Classification Models](https://mlsanity.com/paper/2509.26521/) (2025)
- [Probabilistic Multilabel Graphical Modelling of Motif Transformations in Symbolic Music](https://mlsanity.com/paper/2603.26478/) (2026)

## Abstract (arXiv)

Cadences are complex structures that have been driving music from the beginning of contrapuntal polyphony until today. Detecting such structures is vital for numerous MIR tasks such as musicological analysis, key detection, or music segmentation. However, automatic cadence detection remains challenging mainly because it involves a combination of high-level musical elements like harmony, voice leading, and rhythm. In this work, we present a graph representation of symbolic scores as an intermediate means to solve the cadence detection task. We approach cadence detection as an imbalanced node classification problem using a Graph Convolutional Network. We obtain results that are roughly on par with the state of the art, and we present a model capable of making predictions at multiple levels of granularity, from individual notes to beats, thanks to the fine-grained, note-by-note representation. Moreover, our experiments suggest that graph convolution can learn non-local features that assist in cadence detection, freeing us from the need of having to devise specialized features that encode non-local context. We argue that this general approach to modeling musical scores and classification tasks has a number of potential advantages, beyond the specific recognition task presented here.