  Accompaniment arrangement is a difficult music generation task involving
intertwined constraints of melody, harmony, texture, and music structure.
Existing models are not yet able to capture all these constraints effectively,
especially for long-term music generation. To address this problem, we propose
AccoMontage, an accompaniment arrangement system for whole pieces of music
through unifying phrase selection and neural style transfer. We focus on
generating piano accompaniments for folk/pop songs based on a lead sheet (i.e.,
melody with chord progression). Specifically, AccoMontage first retrieves
phrase montages from a database while recombining them structurally using
dynamic programming. Second, chords of the retrieved phrases are manipulated to
match the lead sheet via style transfer. Lastly, the system offers controls
over the generation process. In contrast to pure learning-based approaches,
AccoMontage introduces a novel hybrid pathway, in which rule-based optimization
and deep learning are both leveraged to complement each other for high-quality
generation. Experiments show that our model generates well-structured
accompaniment with delicate texture, significantly outperforming the baselines.
