Multi-Genre Symbolic Music Generation using Deep Convolutional Generative Adversarial Network

Abstract

Music is an art that uses sound to convey emotions and ideas. It is a universal language that transcends cultural boundaries and can move and inspire individuals of all ages and cultures. As with every art form, the generation of music is a complex and challenging task. Despite the challenges, music generation has made considerable strides in recent years, owing to the application of artificial intelligence and machine learning. However, most research was focused on the generation of only one genre of music, i.e., classical, jazz, etc., while there are more than 40 genres of music, each with sub-genres. This paper proposes a model for multi-genre music generation using Generative Adversarial Networks (GAN). Considering symbolic music, MIDI tracks were converted into piano-roll form after the extraction of musical information. Subsequently, a GAN based model was trained to learn the distribution of training data, and it generates new data using the learned parameters. Generated music was evaluated based on a survey of musicians and professionals. The survey results validate the GAN’s ability to generate music of multiple genres.