CULTURAL PRESERVATION THROUGH AI-GENERATED FOLK MUSIC - Choudhary, Sunila; Pareek, Shalinee; Purohit, Shardha; Ambhore, Vishal; Roy, Ashutosh; Menaria, Sumitra (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Digital preservation of cultures needs to be approached creatively to protect the traditional knowledge of the culture and keeping it relevant to the generations to come. This paper suggests an artificial intelligence-based model of folk music culture creation, analysis and renewal in various cultural areas. Based on the application of deep generative models (recurrent neural networks, Transformers, and diffusion-based audio synthesis), the model learns the rhythmic patterns, melodic contours, scales, and style-specific folk characteristics. The system is a combination of multimodal data that includes audio recordings, coded notations, ethnographic comments, and contextual metadata in order to recreate culturally authentic music. The suggested solution helps to preserve the endangered traditions due to the ability to recover motifs automatically, remix them adaptively, and assist with composition regionally. It also plays up the educational and cultural dissemination activities with interactive interfaces that assist the learners to navigate heritage music patterns. The design has ethical considerations, such as the cultural ownership, attribution, and responsible use of AI, to make sure that the generative models do not negatively impact the identities and artistic heritage of the community. In general, the study indicates that AI is a promising prospective partner in cultural preservation because it provides scalable, innovative, and respectful means of maintaining the folk music heritage and empowering the practitioners, educators, and cultural institutions.