  Diffusion models have shown promising results for a wide range of generative
tasks with continuous data, such as image and audio synthesis. However, little
progress has been made on using diffusion models to generate discrete symbolic
music because this new class of generative models are not well suited for
discrete data while its iterative sampling process is computationally
expensive. In this work, we propose a diffusion model combined with a
Generative Adversarial Network, aiming to (i) alleviate one of the remaining
challenges in algorithmic music generation which is the control of generation
towards a target emotion, and (ii) mitigate the slow sampling drawback of
diffusion models applied to symbolic music generation. We first used a trained
Variational Autoencoder to obtain embeddings of a symbolic music dataset with
emotion labels and then used those to train a diffusion model. Our results
demonstrate the successful control of our diffusion model to generate symbolic
music with a desired emotion. Our model achieves several orders of magnitude
improvement in computational cost, requiring merely four time steps to denoise
while the steps required by current state-of-the-art diffusion models for
symbolic music generation is in the order of thousands.
