  Recently, multi-instrument music generation has become a hot topic. Different
from single-instrument generation, multi-instrument generation needs to
consider inter-track harmony besides intra-track coherence. This is usually
achieved by composing note segments from different instruments into a signal
sequence. This composition could be on different scales, such as note, bar, or
track. Most existing work focuses on a particular scale, leading to a shortage
in modeling music with diverse temporal and track dependencies.
  This paper proposes a multi-scale attentive Transformer model to improve the
quality of multi-instrument generation. We first employ multiple Transformer
decoders to learn multi-instrument representations of different scales and then
design an attentive mechanism to fuse the multi-scale information. Experiments
conducted on SOD and LMD datasets show that our model improves both
quantitative and qualitative performance compared to models based on
single-scale information. The source code and some generated samples can be
found at https://github.com/HaRry-qaq/MSAT.
