To apply neural sequence models such as the Transformers to music generation
tasks, one has to represent a piece of music by a sequence of tokens drawn from
a finite set of pre-defined vocabulary. Such a vocabulary usually involves
tokens of various types. For example, to describe a musical note, one needs
separate tokens to indicate the note's pitch, duration, velocity (dynamics),
and placement (onset time) along the time grid. While different types of tokens
may possess different properties, existing models usually treat them equally,
in the same way as modeling words in natural languages. In this paper, we
present a conceptually different approach that explicitly takes into account
the type of the tokens, such as note types and metric types. And, we propose a
new Transformer decoder architecture that uses different feed-forward heads to
model tokens of different types. With an expansion-compression trick, we
convert a piece of music to a sequence of compound words by grouping
neighboring tokens, greatly reducing the length of the token sequences. We show
that the resulting model can be viewed as a learner over dynamic directed
hypergraphs. And, we employ it to learn to compose expressive Pop piano music
of full-song length (involving up to 10K individual tokens per song), both
conditionally and unconditionally. Our experiment shows that, compared to
state-of-the-art models, the proposed model converges 5--10 times faster at
training (i.e., within a day on a single GPU with 11 GB memory), and with
comparable quality in the generated music.

该论文提出了一种新型 Transformer 解码器架构，其用于不同的前馈头来模拟不同类型的 tokens，以及一种扩展 - 压缩技巧将邻近的 tokens 分组成复合单词的序列，表现出比现有模型更快和同等质量的学习能力.