GENERATIVE AI IN CREATIVE INDUSTRIES USING GANS FOR MUSIC AND ART GENERATION WITH HUMAN-AI CO-CREATION

The rapid development of Generative AI, particularly Generative Adversarial Networks (GANs), has revolutionized the creative industries, including music and art generation. Artists and musicians are increasingly integrating AI to co-create novel compositions and artworks, expanding creative boundaries and fostering innovative forms of artistic expression. Despite the promising potential, the adoption of AI in these fields raises concerns regarding creativity, authorship, and the preservation of artistic authenticity. This study explores the application of GANs for music and art generation, focusing on the collaborative potential of Human-AI co-creation. The primary problem lies in the challenge of maintaining creative autonomy while using AI as a tool, as well as addressing concerns about the originality of AI-generated content. In this study, GANs are used to generate music and visual art, with a focus on the generative process in combination with human input. We employ a hybrid model that allows artists and musicians to interact with AI systems, offering feedback and curating results to guide the output. The method incorporates feedback loops where human selections influence the direction of the generation process, ensuring that the final product aligns with human aesthetic preferences and intentions. The results indicate that human-AI collaboration leads to a richer and more diverse output compared to fully AI-driven generation. For example, in music generation, the hybrid model produced compositions with 89% user satisfaction in terms of creativity and relevance. Similarly, in art generation, 85% of participants reported that AI-generated pieces inspired new artistic ideas, showcasing the effectiveness of AI-human synergy in creative fields. The findings highlight the importance of co- creation in ensuring AI-generated content is meaningful and artistically valuable.