Symbolic music generation aims to generate music scores automatically. A
recent trend is to use Transformer or its variants in music generation, which
is, however, suboptimal, because the full attention cannot efficiently model
the typically long music sequences (e.g., over 10,000 tokens), and the existing
models have shortcomings in generating musical repetition structures. In this
paper, we propose Museformer, a Transformer with a novel fine- and
coarse-grained attention for music generation. Specifically, with the
fine-grained attention, a token of a specific bar directly attends to all the
tokens of the bars that are most relevant to music structures (e.g., the
previous 1st, 2nd, 4th and 8th bars, selected via similarity statistics); with
the coarse-grained attention, a token only attends to the summarization of the
other bars rather than each token of them so as to reduce the computational
cost. The advantages are two-fold. First, it can capture both music
structure-related correlations via the fine-grained attention, and other
contextual information via the coarse-grained attention. Second, it is
efficient and can model over 3X longer music sequences compared to its
full-attention counterpart. Both objective and subjective experimental results
demonstrate its ability to generate long music sequences with high quality and
better structures.

提出了一种新颖的 Transformer 模型 Museformer，它采用了细粒度和粗粒度的注意力机制来生成音乐，能够通过细粒度注意力机制捕捉音乐结构相关的关联，通过粗粒度注意力机制捕捉其他上下文信息，并且可以比完全注意力机制生成长度超过 3 倍的高质量音乐序列。