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.

本文提出了一种数据驱动的框架，以将音乐序列 解析为依赖树，该系统不依赖于任何特定的符号语法，可以同时考虑多种音乐特征，并利用序列上下文信息，可以轻松集成到现代深度学习管道中。在两个音乐数据集上测试表明，该方法优于先前的方法。