Evaluating Convolutional and U-Net Architectures for Tonnetz-Based Music Generation and Prediction

This paper evaluates the performance of a simple convolutional neural network (CNN) and a modified U-Net model for predicting and generating musical chords using Tonnetz graph representations. The simple CNN provides a baseline for convolutional models, while the U-Net, adapted for smaller grids, includes skip connections to improve performance. Results show that the U-Net excels in individual chord prediction but struggles with key changes and long-term generation. The simple CNN performs better in a continuous generation but suffers from repetitive outputs. The study suggests future work could involve advanced methods like diffusion models to enhance musical generation.