Generating Fingerings for Piano Music with Model-Based Reinforcement Learning

Abstract

The piano fingering annotation task refers to assigning finger labels to notes in piano sheet music. Good fingering helps improve the smoothness and musicality of piano performance. In this paper, we propose a method for automatically generating piano fingering using a model-based reinforcement learning algorithm. We treat fingering annotation as a partial constraint combinatorial optimization problem and establish an environment model for the piano performance process based on prior knowledge. We design a reward function based on the principle of minimal motion and use reinforcement learning algorithms to decide the optimal fingering combinations. Our innovation lies in establishing a more realistic environment model and adopting a model-based reinforcement learning approach, compared to model-free methods, to enhance the utilization of samples. We also propose a music score segmentation method to parallelize the fingering annotation task. The experimental section shows that our method achieves good results in eliminating physically impossible fingerings and reducing the amount of finger motion required in piano performance.