We propose AccoMontage-3, a symbolic music automation system capable of
generating multi-track, full-band accompaniment based on the input of a lead
melody with chords (i.e., a lead sheet). The system contains three modular
components, each modelling a vital aspect of full-band composition. The first
component is a piano arranger that generates piano accompaniment for the lead
sheet by transferring texture styles to the chords using latent chord-texture
disentanglement and heuristic retrieval of texture donors. The second component
orchestrates the piano accompaniment score into full-band arrangement according
to the orchestration style encoded by individual track functions. The third
component, which connects the previous two, is a prior model characterizing the
global structure of orchestration style over the whole piece of music. From end
to end, the system learns to generate full-band accompaniment in a
self-supervised fashion, applying style transfer at two levels of polyphonic
composition: texture and orchestration. Experiments show that our system
outperforms the baselines significantly, and the modular design offers
effective controls in a musically meaningful way.

我们提出了 AccoMontage-3，一种能够根据主旋律和和弦（即乐谱）生成多轨全乐队伴奏的符号音乐自动化系统。该系统由三个模块组成，分别模拟全乐队作曲的三个重要方面：钢琴编曲、全乐队编曲和编曲风格建模。实验证明，我们的系统明显优于基准模型，并且模块化设计以音乐上有意义的方式提供了有效的控制。

AccoMontage-3：通过连续风格转换和多轨功能先验的全频伴奏编排

AccoMontage-3: Full-Band Accompaniment Arrangement via Sequential Style  Transfer and Multi-Track Function Prior

Even with strong sequence models like Transformers, generating expressive
piano performances with long-range musical structures remains challenging.
Meanwhile, methods to compose well-structured melodies or lead sheets (melody +
chords), i.e., simpler forms of music, gained more success. Observing the
above, we devise a two-stage Transformer-based framework that Composes a lead
sheet first, and then Embellishes it with accompaniment and expressive touches.
Such a factorization also enables pretraining on non-piano data. Our objective
and subjective experiments show that Compose & Embellish shrinks the gap in
structureness between a current state of the art and real performances by half,
and improves other musical aspects such as richness and coherence as well.

本文提出了一种基于 Transformer 模型的两阶段框架，首先用 Compose & Embellish 方法创作出一个简单音乐谱，然后加入 Begleitungen 及其他表现手法， 明显缩小了当前技术与真实表演之间的差距，同时提高了乐曲的丰富性和连贯性，也可以对非钢琴数据进行预训练。