# Predicting Music Hierarchies with a Graph-Based Neural Decoder

arXiv: https://arxiv.org/abs/2306.16955 · HTML: https://mlsanity.com/paper/2306.16955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 29 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Simpler but More Accurate Semantic Dependency Parsing](https://mlsanity.com/paper/1807.01396/) (2018)
- [Second-Order Semantic Dependency Parsing with End-to-End Neural Networks](https://mlsanity.com/paper/1906.07880/) (2019)
- [Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT](https://mlsanity.com/paper/1908.04943/) (2019)
- [Byte Pair Encoding for Symbolic Music](https://mlsanity.com/paper/2301.11975/) (2023)
- [Transition-based Semantic Dependency Parsing with Pointer Networks](https://mlsanity.com/paper/2005.13344/) (2020)
- [Partitura: A Python Package for Symbolic Music Processing](https://mlsanity.com/paper/2206.01071/) (2022)
- [Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier](https://mlsanity.com/paper/2208.12485/) (2022)
- [A Survey of Syntactic-Semantic Parsing Based on Constituent and Dependency Structures](https://mlsanity.com/paper/2006.11056/) (2020)
- [On the Veracity of Local, Model-agnostic Explanations in Audio Classification: Targeted Investigations with Adversarial Examples](https://mlsanity.com/paper/2107.09045/) (2021)

## Built on by (most cited first, 2 of 2)

- [Syntax-Guided Transformers: Elevating Compositional Generalization and Grounding in Multimodal Environments](https://mlsanity.com/paper/2311.04364/) (2023)
- [AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation](https://mlsanity.com/paper/2512.18232/) (2025)

## Abstract (arXiv)

This paper describes a data-driven framework to parse musical sequences into dependency trees, which are hierarchical structures used in music cognition research and music analysis. The parsing involves two steps. First, the input sequence is passed through a transformer encoder to enrich it with contextual information. Then, a classifier filters the graph of all possible dependency arcs to produce the dependency tree. One major benefit of this system is that it can be easily integrated into modern deep-learning pipelines. Moreover, since it does not rely on any particular symbolic grammar, it can consider multiple musical features simultaneously, make use of sequential context information, and produce partial results for noisy inputs. We test our approach on two datasets of musical trees -- time-span trees of monophonic note sequences and harmonic trees of jazz chord sequences -- and show that our approach outperforms previous methods.