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Estimating the performance difficulty of a musical score is crucial in musiceducation for adequately designing the learning curriculum of the students.Although the Music Information Retrieval community has recently shown interestin this task, existing approaches mainly use machine-readable scores, leavingthe broader case of sheet music images unaddressed. Based on previous worksinvolving sheet music images, we use a mid-level representation, bootleg score,describing notehead positions relative to staff lines coupled with atransformer model. This architecture is adapted to our task by introducing anencoding scheme that reduces the encoded sequence length to one-eighth of theoriginal size. In terms of evaluation, we consider five datasets -- more than7500 scores with up to 9 difficulty levels -- , two of them particularlycompiled for this work. The results obtained when pretraining the scheme on theIMSLP corpus and fine-tuning it on the considered datasets prove the proposal'svalidity, achieving the best-performing model with a balanced accuracy of40.34\% and a mean square error of 1.33. Finally, we provide access to ourcode, data, and models for transparency and reproducibility.

Predicting performance difficulty from piano sheet music images