Score difficulty analysis for piano performance education based on fingering

Comunicació presentada a: 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), celebrat del 22 al 27 de maig de 2022 a Singapur.In this paper, we introduce score difficulty classification as a subtask of music information retrieval (MIR), which may be used in music education technologies, for personalised curriculum generation, and score retrieval. We introduce a novel dataset for our task, Mikrokosmos-difficulty, containing 147 piano pieces in symbolic representation and the corresponding difficulty labels derived by its composer Bela Bart ´ ok and the publishers. As part of our ´ methodology, we propose piano technique feature representations based on different piano fingering algorithms. We use these features as input for two classifiers: a Gated Recurrent Unit neural network (GRU) with attention mechanism and gradient-boosted trees trained on score segments. We show that for our dataset fingering based features perform better than a simple baseline considering solely the notes in the score. Furthermore, the GRU with attention mechanism classifier surpasses the gradient-boosted trees. Our proposed models are interpretable and are capable of generating difficulty feedback both locally, on short term segments, and globally, for whole pieces. Code, datasets, models, and an online demo are made available for reproducibility.This research is funded by the project Musical AI - PID2019- 111403GB-I00/AEI/10.13039/501100011033 funded by the Spanish Ministerio de Ciencia, Innovacion y Universidades (MCIU) and ´ the Agencia Estatal de Investigacion (AEI)