  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.
