Towards Explainable and Interpretable Musical Difficulty Estimation: A Parameter-efficient Approach

TL;DR

This paper introduces a novel, interpretable, and parameter-efficient model for estimating musical difficulty from symbolic music data, enhancing transparency and accuracy in educational contexts.

Contribution

It presents a white-box model utilizing explainable descriptors that outperform previous methods and emulate educational rubrics for difficulty assessment.

Findings

Achieved 41.4% accuracy in 9-class piano difficulty classification

MSE of 1.7 indicates precise difficulty estimation

Model provides interpretable results similar to educational rubrics

Abstract

Estimating music piece difficulty is important for organizing educational music collections. This process could be partially automatized to facilitate the educator's role. Nevertheless, the decisions performed by prevalent deep-learning models are hardly understandable, which may impair the acceptance of such a technology in music education curricula. Our work employs explainable descriptors for difficulty estimation in symbolic music representations. Furthermore, through a novel parameter-efficient white-box model, we outperform previous efforts while delivering interpretable results. These comprehensible outcomes emulate the functionality of a rubric, a tool widely used in music education. Our approach, evaluated in piano repertoire categorized in 9 classes, achieved 41.4% accuracy independently, with a mean squared error (MSE) of 1.7, showing precise difficulty estimation. Through our baseline, we illustrate how building on top of past research can offer alternatives for music difficulty assessment which are explainable and interpretable. With this, we aim to promote a more effective communication between the Music Information Retrieval (MIR) community and the music education one.