Predicting Music Hierarchies with a Graph-Based Neural Decoder

TL;DR

This paper introduces a graph-based neural decoder that parses musical sequences into hierarchical dependency trees, leveraging transformer encodings and classifiers, and demonstrates superior performance on musical datasets.

Contribution

The paper presents a novel neural framework for parsing music into dependency trees that integrates seamlessly with deep learning and handles noisy inputs effectively.

Findings

Outperforms previous methods on musical tree datasets

Can process multiple musical features simultaneously

Handles noisy inputs and partial results

Abstract

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.