Exploring the Evolution of GenAI in Music Composition

Abstract

Crafting musical compositions presents a unique set of challenges, divergent from those encountered in visual art forms. The temporal nature of music necessitates models proficient in handling time dynamics. Moreover, compositions often encompass multiple tracks, each characterized by its own temporal intricacies, demanding an intricate approach to their interdependent evolution. Unlike static visual imagery, musical notes are sequenced, commonly organized into chords or melodies, imposing a requirement for specialized chronological structures. This paper extensively delves into the evolutionary journey of GenAI within the domain of sequential generative adversarial networks (GANs) tailored specifically for music composition. We introduce a suite of novel models meticulously crafted to address the nuances of musical generation, exploring their efficacy in producing complex multi-track compositions. Our investigation centers on a comprehensive analysis of the evolutionary trajectory of these models, scrutinizing their capacity to autonomously generate cohesive musical sequences across diverse tracks. Through rigorous empirical evaluations, we substantiate the capabilities of our models to produce compelling musical segments sans human intervention. Furthermore, we delve into intricate technical discussions, elucidating the underlying mechanisms driving the generation process, including the intricate interplay of neural architectures and training methodologies. In addition to empirical validation, we conduct detailed user studies, garnering insights into the subjective perception of the generated compositions. Moreover, we delve into the realm of human-AI collaboration in music generation, unveiling the potential of GenAI to complement human compositions by seamlessly providing harmonious accompaniment, thereby bridging the gap between artistic creativity and computational progress.