Predicting Music Hierarchies with a Graph-Based Neural Decoder
This work addresses the need for automated music analysis tools in music cognition and research, offering an incremental improvement by integrating deep learning without relying on symbolic grammars.
The paper tackles the problem of parsing musical sequences into hierarchical dependency trees by introducing a data-driven framework that uses a transformer encoder and a graph-based classifier, achieving superior performance over previous methods on two datasets of musical trees.
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.