Deep recurrent music writer: Memory-enhanced variational autoencoder-based musical score composition and an objective measure

In recent years, there has been an increasing interest in music generation using machine learning techniques typically used for classification or regression tasks. This is a field still in its infancy, and most attempts are still characterized by the imposition of many restrictions to the music composition process in order to favor the creation of 'interesting' outputs. Furthermore, and most importantly, none of the past attempts has focused on developing objective measures to evaluate the music composed, which would allow to evaluate the pieces composed against a predetermined standard as well as permitting to fine-tune models for better 'performance' and music composition goals. In this work, we intend to advance state-of-the-art in this area by introducing and evaluating a new metric for an objective assessment of the quality of the generated pieces. We will use this measure to evaluate the outputs of a truly generative model based on Variational Autoencoders that we apply here to automated music composition. Using our metric, we demonstrate that our model can generate music pieces that follow general stylistic characteristics of a given composer or musical genre. Additionally, we use this measure to investigate the impact of various parameters and model architectures on the compositional process and output.