Existing melody harmonization models have made great progress in improving
the quality of generated harmonies, but most of them ignored the emotions
beneath the music. Meanwhile, the variability of harmonies generated by
previous methods is insufficient. To solve these problems, we propose a novel
LSTM-based Hierarchical Variational Auto-Encoder (LHVAE) to investigate the
influence of emotional conditions on melody harmonization, while improving the
quality of generated harmonies and capturing the abundant variability of chord
progressions. Specifically, LHVAE incorporates latent variables and emotional
conditions at different levels (piece- and bar-level) to model the global and
local music properties. Additionally, we introduce an attention-based melody
context vector at each step to better learn the correspondence between melodies
and harmonies. Experimental results of the objective evaluation show that our
proposed model outperforms other LSTM-based models. Through subjective
evaluation, we conclude that only altering the chords hardly changes the
overall emotion of the music. The qualitative analysis demonstrates the ability
of our model to generate variable harmonies.

提出了新型的 LSTM 分层变分自编码器，以研究情感条件对旋律和声化的影响，提高生成的和声质量并捕获和弦进程的丰富变化。