Deep Learning for Expressive Music Generation

Abstract

In the last decade, Deep Learning (DL) algorithms have been increasing its popularity in several fields such as computer vision, speech recognition, natural language processing and many others. DL models, however, are not limited to scientific domains as they have recently been applied to content generation in diverse art forms - both in the generation of novel contents and as co-creative tools. Artificial music generation is one of the fields where DL architectures have been applied. They have been mostly used to create new compositions exhibiting promising results when compared to human compositions. Despite this, the majority of these artificial pieces lack some expression when compared to music compositions performed by humans. In this document, we propose a system capable of artificially generating expressive music compositions. Our main goal is to improve the quality of the musical compositions generated by the artificial system by exploring perceptually relevant musical elements such as note velocity and duration. To assess this hypothesis, we perform user tests. Results suggest that expressive elements such as duration and velocity are key aspects in a music composition expression, making the ones who include these preferable to non-expressive ones.