  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. Objective experimental results show that our proposed model
outperforms other LSTM-based models. Through subjective evaluation, we conclude
that only altering the types of chords hardly changes the overall emotion of
the music. The qualitative analysis demonstrates the ability of our model to
generate variable harmonies.
