Music Creation Technology Based on Generative Adversarial Network

Abstract

Music creation technology has witnessed significant advancements with the emergence of Generative Adversarial Networks (GANs), a form of artificial intelligence renowned for its ability to generate high-quality, diverse output. In this study, we explore the application of GANs in music composition, aiming to assess the performance and creative potential of GAN-based music generation systems. Leveraging a dataset comprising MIDI representations of classical piano compositions, we trained a Wasserstein GAN with Gradient Penalty (WGAN-GP) architecture to generate piano roll representations of music. Our results demonstrate that the generated music closely approximates the distribution of real compositions, as evidenced by the low Fréchet Inception Distance (FID) score. Furthermore, the high Inception Score (IS) indicates that the generated music exhibits diversity and richness, showcasing the model's ability to explore a wide range of musical styles and expressions. Qualitative assessment by human judges further validates the artistic merit and subjective appeal of the generated music, highlighting its coherence, expressiveness, and novelty. However, challenges such as ethical considerations surrounding AI-generated music and the subjective nature of musical creativity warrant careful consideration. Moving forward, the integration of GAN-based music generation technology holds promise for revolutionizing music composition, education, and cross-cultural collaboration, while emphasizing the importance of interdisciplinary collaboration and ethical stewardship in shaping the future of AI-driven music technology.