# Perception-Inspired Graph Convolution for Music Understanding Tasks

arXiv: https://arxiv.org/abs/2405.09224 · HTML: https://mlsanity.com/paper/2405.09224/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 15 May 2024.

## Builds on (most cited first, 21 of 21)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [Residual Gated Graph ConvNets](https://mlsanity.com/paper/1711.07553/) (2017)
- [Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1911.07323/) (2019)
- [Point Convolutional Neural Networks by Extension Operators](https://mlsanity.com/paper/1803.10091/) (2018)
- [An optimal transportation approach for assessing almost stochastic order](https://mlsanity.com/paper/1705.01788/) (2017)
- [miditok: A Python package for MIDI file tokenization](https://mlsanity.com/paper/2310.17202/) (2023)
- [Symbolic Music Representations for Classification Tasks: A Systematic Evaluation](https://mlsanity.com/paper/2309.02567/) (2023)
- [Cadence Detection in Symbolic Classical Music using Graph Neural Networks](https://mlsanity.com/paper/2208.14819/) (2022)
- [PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation](https://mlsanity.com/paper/2107.14009/) (2021)
- [Roman Numeral Analysis with Graph Neural Networks: Onset-wise Predictions from Note-wise Features](https://mlsanity.com/paper/2307.03544/) (2023)
- [Learning Complex Basis Functions for Invariant Representations of Audio](https://mlsanity.com/paper/1907.05982/) (2019)
- [Musical Voice Separation as Link Prediction: Modeling a Musical Perception Task as a Multi-Trajectory Tracking Problem](https://mlsanity.com/paper/2304.14848/) (2023)
- [Downbeat Tracking with Tempo-Invariant Convolutional Neural Networks](https://mlsanity.com/paper/2102.02282/) (2021)
- [Modeling Harmony with Skip-Grams](https://mlsanity.com/paper/1707.04457/) (2017)
- [The match file format: Encoding Alignments between Scores and Performances](https://mlsanity.com/paper/2206.01104/) (2022)
- [Modeling Music Modality with a Key-Class Invariant Pitch Chroma CNN](https://mlsanity.com/paper/1906.07145/) (2019)

## Built on by (most cited first, 1 of 1)

- [Style-based Composer Identification and Attribution of Symbolic Music Scores: a Systematic Survey](https://mlsanity.com/paper/2506.12440/) (2025)

## Abstract (arXiv)

We propose a new graph convolutional block, called MusGConv, specifically designed for the efficient processing of musical score data and motivated by general perceptual principles. It focuses on two fundamental dimensions of music, pitch and rhythm, and considers both relative and absolute representations of these components. We evaluate our approach on four different musical understanding problems: monophonic voice separation, harmonic analysis, cadence detection, and composer identification which, in abstract terms, translate to different graph learning problems, namely, node classification, link prediction, and graph classification. Our experiments demonstrate that MusGConv improves the performance on three of the aforementioned tasks while being conceptually very simple and efficient. We interpret this as evidence that it is beneficial to include perception-informed processing of fundamental musical concepts when developing graph network applications on musical score data.