A hybrid deep learning approach for musical difficulty estimation of piano symbolic music

Abstract

Musical difficulty estimation is an essential part of musical learning. Without a precise estimate, a music learner cannot choose a piece to play according to their current level. This problem is highly complicated for its subjectivity and data scarcity. In this study, we investigate deep learning approaches to solve this problem. Our pipeline can be summarized as follows: firstly, we convert the symbolic music MIDI file of a piano performance to piano roll representation. Secondly, the piano roll is divided into smaller parts. Finally, a model is trained on parts accompanied by the corresponding difficulty labels. We test our models on both complete and partial track difficulty classification problems. Multiple deep convolutional neural networks are proposed and evaluated. Accompanied with handcrafted features, the proposed hybrid deep model yields a relative F1 score improvement of more than 10% compared to previous studies, achieving a state-of-the-art F1 score of 76.26%. Besides the direct application of the work to classify musical pieces, the promising results can be a starting point for more complicated applications, including automated difficulty-controlled music generation.

Keywords

• Machine learning
• Deep learning
• Classification
• Neural networks
• Computer music