Predicting Music Hierarchies with a Graph-Based Neural Decoder
Francesco Foscarin Daniel Harasim Gerhard Widmer
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.