Integrating Signal Processing and Computational Creativity: A Theoretical Framework for Modern Music Composition

The field of music composition is undergoing a significant transformation due to advancements in artificial intelligence. This paper explores a theoretical framework for modern music composition by integrating signal processing techniques with computational creativity. The author has sought to analyze how AI, particularly through the application of Differentiable Digital Signal Processing (DDSP) and neural networks, can interpret and generate music based on its structural principles. The discussion includes the use of Markov models to describe music structure as probability patterns, enabling AI to create new pieces while adhering to established principles. Furthermore, the author examines the incorporation of brain-inspired architectures in neural networks to mimic cognitive processes such as key and mode recognition, fostering a synergistic relationship between human composers and AI. This paper also addresses the challenges faced in AI music composition, including long-term structure modeling, dataset bias, and the lack of 'humanness' in AI-generated music. Finally, we discuss the potential for human-AI collaborative composition, emphasizing the evolving role of AI as a partner rather than a replacement for human creativity. This research highlights the ongoing progress and future directions in leveraging AI to enhance and redefine the music composition process.