Morsi, AliaZhang, HuanMaezawa, AkiraDixon, Simon2024-07-032024-07-032024-07-03Morsi A, Zhang H, Maezawa A, Dixon S, Serra X. Simulating piano performance mistakes for music learning. Paper presented at: 21st Sound and Music Computing Conference SMC 2024; 2024 July 4-6; Porto, Portugal.http://hdl.handle.net/10230/60657This work has been accepted at 21st Sound and Music Computing Conference SMC 2024, at Porto, Portugal. July 4-6, 2024.The development of machine-learning based technologies to support music instrument learning needs large-scale datasets that capture the different stages of learning in a manner that is both realistic and computation-friendly. We are interested in modeling the mistakes of beginnerintermediate piano performances in practice or work-inprogress settings. In the absence of large-scale data representing our target case, our approach is to start by understanding such mistakes from real data and then provide a methodology for their simulation, thus creating synthetic data to support the training of performance assessment models. The main goals of this paper are: a) to propose a taxonomy of performance mistakes, specifically apt for simulating or reproducing/recreating them on mistake-free MIDI performances, and b) to provide a pipeline for creating synthetic datasets based on the former. We incorporate prior research in related contexts to facilitate the understanding of common mistake behaviours. Then, we design a hierarchical mistake taxonomy to categorize two real-world datasets capturing relevant piano performance contexts. Finally, we discuss our approach with 3 music teachers through a listening test and subsequent discussions.application/pdfengCopyright: © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.info:eu-repo/semantics/reportinfo:eu-repo/semantics/openAccess