Symbolic Music Generation

Symbolic music generation aims to create musical scores computationally, offering a powerful tool for music composition and analysis. Current research heavily utilizes deep learning models, particularly transformer-based architectures and diffusion models, often incorporating techniques like pre-training, fine-tuning, and various forms of control mechanisms (e.g., text prompts, metadata, constraints) to enhance both the quality and controllability of generated music. This field is significant for its potential to augment human creativity, assist in music education, and advance our understanding of musical structure and cognition.