Discovering and exploring the underlying structure of multi-instrumental music using
learning-based approaches remains an open problem. We extend the recent MusicVAE …

Research on automatic music generation has seen great progress due to the development
of deep neural networks. However, the generation of multi-instrument music of arbitrary …

This chapter reflects on about 10 years of research in AI-assisted music composition, in
particular during the Flow Machines project. We reflect on the motivations for such a project …

This chapter explores the creative possibilities offered by latent spaces. Latent spaces are
machine-learnt maps representing large media datasets such as images and sound. With a …

In this work, we propose a flexible method for generating variations of discrete sequences in
which tokens can be grouped into basic units, like sentences in a text or bars in music. More …

In recent years, machine learning, and in particular generative adversarial neural networks
(GANs) and attention-based neural networks (transformers), have been successfully used to …

This thesis discusses the use of deep generative models for symbolic music generation. We
will be focused on devising interactive generative models which are able to create new …

Distances on symbolic musical sequences are needed for a variety of applications, from
music retrieval to automatic music generation. These musical sequences belong to a given …

During the past few decades, we have seen the invasion of deep generative models in the
areas of image processing and text generating. Tremendous practical applications are …