Generative AI models for music and the arts in general are increasingly
complex and hard to understand. The field of eXplainable AI (XAI) seeks to make
complex and opaque AI models such as neural networks more understandable to
people. One approach to making generative AI models more understandable is to
impose a small number of semantically meaningful attributes on generative AI
models. This paper contributes a systematic examination of the impact that
different combinations of Variational Auto-Encoder models (MeasureVAE and
AdversarialVAE), configurations of latent space in the AI model (from 4 to 256
latent dimensions), and training datasets (Irish folk, Turkish folk, Classical,
and pop) have on music generation performance when 2 or 4 meaningful musical
attributes are imposed on the generative model. To date there have been no
systematic comparisons of such models at this level of combinatorial detail.
Our findings show that MeasureVAE has better reconstruction performance than
AdversarialVAE which has better musical attribute independence. Results
demonstrate that MeasureVAE was able to generate music across music genres with
interpretable musical dimensions of control, and performs best with low
complexity music such a pop and rock. We recommend that a 32 or 64 latent
dimensional space is optimal for 4 regularised dimensions when using MeasureVAE
to generate music across genres. Our results are the first detailed comparisons
of configurations of state-of-the-art generative AI models for music and can be
used to help select and configure AI models, musical features, and datasets for
more understandable generation of music.

这篇论文通过对不同组合的 Variational Auto-Encoder 模型、AI 模型的潜在空间配置和训练数据集进行系统研究，表明 MeasureVAE 在音乐生成性能方面具有更好的重构表现，AdversarialVAE 在音乐属性独立性方面表现更好。结果展示了 MeasureVAE 能够通过可解释的音乐控制维度生成跨音乐流派的音乐，并且在生成简单的流行和摇滚音乐时表现最佳。建议在使用 MeasureVAE 生成跨流派音乐时，4 个正则化维度的 32 或 64 个潜在空间尺寸是最佳选择。这些结果是关于音乐生成的最详细的现代生成 AI 模型配置的比较，并可用于选择和配置 AI 模型、音乐特征和数据集以实现更易理解的音乐生成。