Definitive embeddings remain a fundamental challenge of computational
musicology for symbolic music in deep learning today. Analogous to natural
language, music can be modeled as a sequence of tokens. This motivates the
majority of existing solutions to explore the utilization of word embedding
models to build music embeddings. However, music differs from natural languages
in two key aspects: (1) musical token is multi-faceted -- it comprises of
pitch, rhythm and dynamics information; and (2) musical context is
two-dimensional -- each musical token is dependent on both melodic and harmonic
contexts. In this work, we provide a comprehensive solution by proposing a
novel framework named PiRhDy that integrates pitch, rhythm, and dynamics
information seamlessly. PiRhDy adopts a hierarchical strategy which can be
decomposed into two steps: (1) token (i.e., note event) modeling, which
separately represents pitch, rhythm, and dynamics and integrates them into a
single token embedding; and (2) context modeling, which utilizes melodic and
harmonic knowledge to train the token embedding. A thorough study was made on
each component and sub-strategy of PiRhDy. We further validate our embeddings
in three downstream tasks -- melody completion, accompaniment suggestion, and
genre classification. Results indicate a significant advancement of the neural
approach towards symbolic music as well as PiRhDy's potential as a pretrained
tool for a broad range of symbolic music applications.

提出了一种名为 PiRhDy 的新框架，该框架将音高、节奏和动态信息无缝地整合在一起，并利用音乐的旋律和和声知识训练令牌嵌入，表现出对符号音乐的显着进步，并有潜力成为各种符号音乐应用程序的预训练工具。