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

arXiv: https://arxiv.org/abs/2408.00473 · HTML: https://mlsanity.com/paper/2408.00473/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 37 references that are in the map.
- First posted on arXiv 1 Aug 2024.

## Builds on (most cited first, 7 of 7)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Learning Optimized Risk Scores](https://mlsanity.com/paper/1610.00168/) (2016)
- [Symbolic Music Representations for Classification Tasks: A Systematic Evaluation](https://mlsanity.com/paper/2309.02567/) (2023)
- [Score difficulty analysis for piano performance education based on fingering](https://mlsanity.com/paper/2203.13010/) (2022)
- [Combining piano performance dimensions for score difficulty classification](https://mlsanity.com/paper/2306.08480/) (2023)
- [Predicting performance difficulty from piano sheet music images](https://mlsanity.com/paper/2309.16287/) (2023)
- [Can Audio Reveal Music Performance Difficulty? Insights from the Piano Syllabus Dataset](https://mlsanity.com/paper/2403.03947/) (2024)

## Built on by (most cited first, 1 of 1)

- [MuSViT: A Foundation Vision Model for Sheet Music Representation](https://mlsanity.com/paper/2606.31811/) (2026)

## Abstract (arXiv)

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.