A great number of deep learning based models have been recently proposed for
automatic music composition. Among these models, the Transformer stands out as
a prominent approach for generating expressive classical piano performance with
a coherent structure of up to one minute. The model is powerful in that it
learns abstractions of data on its own, without much human-imposed domain
knowledge or constraints. In contrast with this general approach, this paper
shows that Transformers can do even better for music modeling, when we improve
the way a musical score is converted into the data fed to a Transformer model.
In particular, we seek to impose a metrical structure in the input data, so
that Transformers can be more easily aware of the beat-bar-phrase hierarchical
structure in music. The new data representation maintains the flexibility of
local tempo changes, and provides hurdles to control the rhythmic and harmonic
structure of music. With this approach, we build a Pop Music Transformer that
composes Pop piano music with better rhythmic structure than existing
Transformer models.

本文提出将乐谱数据表示成具有韵律结构的形式，通过开发更好的数据输入方式，我们建立了一个节奏更流畅的 Pop 钢琴音乐生成模型 - Pop Music Transformer。