The Transformative Role of Generative AI in Music Production and Creation: A CiteSpace-Based Bibliometric Analysis

The rapid advancement of generative artificial intelligence (Generative AI) is reshaping the landscape of music production, transforming creative methodologies, technological processes, and aesthetic value systems.Based on 630 publications retrieved from the Web of Science Core Collection, this study employs CiteSpace (version 6.4.R1) to perform a systematic bibliometric and visualization analysis of Generative AI in music.The overall network shows a modularity Q value of 0.7326 and a mean silhouette coefficient of 0.9144, indicating high structural and clustering reliability.The field exhibits a progressive evolution through three stages: early explorations in algorithmic composition and model design, mid-term expansion driven by deep learning and multimodal generation, and recent advances in human-AI co-creation and interactive composition.The United States and China form a dual-core collaboration structure, while Queen Mary University of London and Tsinghua University emerge as leading institutions.The keyword clustering results indicate that the research field is structured around three interrelated thematic groups, collectively illustrating a paradigm shift from technology-driven exploration toward a more integrated trajectory that encompasses artistic expression and ethical reflection.This study not only maps the dynamic knowledge structure of Generative AI music research but also proposes a "Technology-Creation-Value-Feedback" conceptual framework, offering quantitative insights and strategic implications for artistic practice, industrial innovation, and copyright governance.