Data-Driven Generative Adversarial Networks for Music Composition: Models, Data Representation, and Application

Abstract

With the rapid advancement of artificial intelligence technology, generative adversarial networks (GANs) have demonstrated immense potential in the field of music composition. This paper reviews the theoretical foundations, model research progress, application analysis, and challenges faced by GANs in music composition, while also outlining future research directions. First, the basic structure of GANs, music data representation methods, and the principles of GAN-generated music are introduced, laying the theoretical foundation for subsequent research. Next, the current state of GAN-based music composition models—including sequence generation, image-to-sequence conversion, and models integrating other technologies—is elaborated in detail, with comparative analyses of these approaches. Subsequently, the application of GANs in music style imitation and transformation, musical improvisation, and music creation assistance is explored in depth, demonstrating their broad prospects in the musical domain. However, GANs in music composition also face challenges such as unstable model training and generated music lacking emotional expression. Future research can focus on improving model architecture, integrating multimodal information, and enhancing music semantic understanding to advance GANs in the field of music composition.