Year: 2025
Type: article
Abstract: The integration of traditional and contemporary music creates a unique opportunity to foster cultural exchange and innovation. This paper proposes a generative AI framework to blend the intricate melodic and rhythmic elements of Carnatic music with global genres such as Western classical, jazz, and electronic music. Carnatic music, known for its microtonal ragas, complex tala cycles, and improvisational depth, presents both challenges and opportunities for AI-driven fusion. The proposed framework leverages advanced machine learning techniques to synthesize melodies, rhythms, and instrumentation. By aligning Carnatic ragas with global harmonic structures and integrating tala patterns with contemporary rhythms, the framework aims to produce cohesive and culturally resonant compositions. In addition to outlining the framework, the paper proposes an evaluation strategy comprising objective metrics, such as tonal fidelity and rhythmic complexity, and subjective measures like cultural authenticity and listener appeal. Theoretical case studies illustrate the application of the framework in generating innovative cross- cultural compositions. This work highlights the potential of AI in reimagining traditional music while promoting global collaboration, offering new tools for composers, educators, and performers to explore the possibilities of cross-cultural musical fusion. Keywords: AI in Music Fusion, Generative AI, Sequence Modeling, Style Transfer, Harmonic Integration.
DOI: 10.55041/ijsrem28942
Author: Abhinav Balasubramanian
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4407230744