In recent years, the adoption of deep learning techniques has allowed to obtain major breakthroughs in the automatic music generation research field, sparking a renewed interest in generative music. A great deal of work has focused on the possibility of conditioning the generation process in order to be able to create music according to human-understandable parameters. In this paper, we propose a technique for generating chord progressions conditioned on harmonic complexity, as grounded in the Western music theory. More specifically, we consider a pre-existing dataset annotated with the related complexity values and we train two variations of Variational Autoencoders (VAE), namely a Conditional-VAE (CVAE) and a Regressor-based VAE (RVAE), in order to condition the latent space depending on the complexity. Through a listening test, we analyze the effectiveness of the proposed techniques.

中文翻译：

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基于西方和声音乐复杂性的和弦序列生成变分自动编码器

近年来，深度学习技术的采用使得自动音乐生成研究领域取得了重大突破，重新激发了人们对生成音乐的兴趣。大量工作都集中在调节生成过程的可能性上，以便能够根据人类可理解的参数创作音乐。在本文中，我们提出了一种以西方音乐理论为基础，以和声复杂度为条件生成和弦进行的技术。更具体地说，我们考虑使用相关复杂度值注释的预先存在的数据集，并且我们训练变分自动编码器 (VAE) 的两种变体，即条件 VAE (CVAE) 和基于回归器的 VAE (RVAE)，以便条件化潜在空间取决于复杂性。通过听力测试，