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。

基于情感条件的谐声旋律编制与分层变分自编码器

Emotion-Conditioned Melody Harmonization with Hierarchical Variational  Autoencoder

Repetition is a basic indicator of musical structure. This study introduces
new algorithms for identifying musical phrases based on repetition. Phrases
combine to form sections yielding a two-level hierarchical structure.
Automatically detected hierarchical repetition structures reveal significant
interactions between structure and chord progressions, melody and rhythm.
Different levels of hierarchy interact differently, providing evidence that
structural hierarchy plays an important role in music beyond simple notions of
repetition or similarity. Our work suggests new applications for music
generation and music evaluation.

本研究介绍了基于重复的算法，用于识别音乐篇章结构，音乐片段组合成具有两级层次结构的音乐节，层次结构揭示了音乐结构和和弦进行、旋律、节奏之间的重要关联，不同层次结构有不同的相互作用，表明结构层次在音乐中发挥着重要作用，本研究为音乐生成和评估提供了新的应用。

流行音乐中层级结构对旋律、节奏和和声的自动分析及影响

Automatic Analysis and Influence of Hierarchical Structure on Melody,  Rhythm and Harmony in Popular Music

In this paper, we introduce new methods and discuss results of text-based
LSTM (Long Short-Term Memory) networks for automatic music composition. The
proposed network is designed to learn relationships within text documents that
represent chord progressions and drum tracks in two case studies. In the
experiments, word-RNNs (Recurrent Neural Networks) show good results for both
cases, while character-based RNNs (char-RNNs) only succeed to learn chord
progressions. The proposed system can be used for fully automatic composition
or as semi-automatic systems that help humans to compose music by controlling a
diversity parameter of the model.

本文介绍了一种基于文本的 LSTM（长短时记忆）网络的新方法，并讨论了其在自动音乐作曲方面的应用，研究表明采用基于词的 RNNs 对和弦进行学习的效果较好，该系统可用于完全自动的音乐创作或辅助人类通过调节模型的多样性参数来创作音乐。