  This paper proposes a novel Transformer-based model for music score
infilling, to generate a music passage that fills in the gap between given past
and future contexts. While existing infilling approaches can generate a passage
that connects smoothly locally with the given contexts, they do not take into
account the musical form or structure of the music and may therefore generate
overly smooth results. To address this issue, we propose a structure-aware
conditioning approach that employs a novel attention-selecting module to supply
user-provided structure-related information to the Transformer for infilling.
With both objective and subjective evaluations, we show that the proposed model
can harness the structural information effectively and generate melodies in the
style of pop of higher quality than the two existing structure-agnostic
infilling models.
