Innovative Applications of Generative Artificial Intelligence in Music Composition: A Case Study of Multi-Part Instrumental Ensemble

Abstract

Generative Artificial Intelligence (AI) has attracted considerable interest in several fields with growth in different areas, especially in the arts, especially in music production. With respect to the above research questions, this review paper aims to illustrate the uses of AI-driven generative models in making multi-part instrumental ensembles. The case study concentrates on the applicability of generative algorithms for intricate layered compositions making use of deep learning and reinforcement learning techniques that are in synergy for imitating traditional composite ensembles. As for the AI methods being discussed in the paper, there is a focus on the use of neural networks, generative adversarial networks (GANs), and recurrent neural networks (RNNs) and how they can be applied to compose harmony that would reflect stylistic coherence across the genres selected. From this case study, the paper shows how AI can transform the possibility of composition and propose areas that composers have not yet developed and are unlikely to consider within existing limits. It also talks about the drawbacks that act as barriers to applying AI in music production problems, including how to make the pieces created more creative, how to instill emotion, and how to make them sound more human-like. Moreover, the present review explores the marriage between AI and music theory to check how machine algorithms can capture and imitate harmonic, counterpoint, and rhythmic patterns. Overall, therefore, the goal of this paper is to afford a broad and relatively detailed understanding of the manner in which AI can be used to revolutionize the process of music composing with a special emphasis placed on the domain of multi-part instrumental ensembles.