Melody harmonization has long been closely associated with chorales composed
by Johann Sebastian Bach. Previous works rarely emphasised chorale generation
conditioned on chord progressions, and there has been a lack of focus on
assistive compositional tools. In this paper, we first designed a music
representation that encoded chord symbols for chord conditioning, and then
proposed DeepChoir, a melody harmonization system that can generate a four-part
chorale for a given melody conditioned on a chord progression. With
controllable harmonicity, users can control the extent of harmonicity for
generated chorales. Experimental results reveal the effectiveness of the music
representation and the controllability of DeepChoir.

本文提出了 DeepChoir 这样一个旨在生成和声的音乐系统，通过特定的和弦进程生成给定旋律的四声部赞美诗曲子，实验结果表明该系统的音乐表示和深度学习方法的有效性以及生成赞美诗的可控性。

带有可控和声的按和弦演奏的旋律和声化

Chord-Conditioned Melody Harmonization with Controllable Harmonicity

Pop music generation has always been an attractive topic for both musicians
and scientists for a long time. However, automatically composing pop music with
a satisfactory structure is still a challenging issue. In this paper, we
propose to leverage harmony-aware learning for structure-enhanced pop music
generation. On the one hand, one of the participants of harmony, chord,
represents the harmonic set of multiple notes, which is integrated closely with
the spatial structure of music, the texture. On the other hand, the other
participant of harmony, chord progression, usually accompanies the development
of the music, which promotes the temporal structure of music, the form.
Moreover, when chords evolve into chord progression, the texture and form can
be bridged by the harmony naturally, which contributes to the joint learning of
the two structures. Furthermore, we propose the Harmony-Aware Hierarchical
Music Transformer (HAT), which can exploit the structure adaptively from the
music, and make the musical tokens interact hierarchically to enhance the
structure in multi-level musical elements. Experimental results reveal that
compared to the existing methods, HAT owns a much better understanding of the
structure and it can also improve the quality of generated music, especially in
the form and texture.

本文提出了一种基于和声学习的结构增强流行音乐生成方法，并设计了和声感知的分层音乐变形器 (HAT)，可以在多层级音乐元素中自适应地利用结构，从而提高生成的流行音乐的质量。实验结果表明，与现有方法相比，HAT 可以更好地理解结构，尤其是在形式和质地方面可以提高生成音乐的质量。