  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.
