Awarded the Best Paper at the Sound and Music Computing (SMC) 2024

Awarded the Best Paper in Simulating Piano Performance Mistakes for Music Lerning

A research team comprising of Alia Morsi, a summer 2024 intern in the Research and Development Division, Dr. Akira Maezawa of the Research and Development Division and others were awarded the “Best Paper” at the Sound and Music Computing (SMC) conference 2024. The Best Paper is given to the researchers who have made the most outstanding presentation at the conference.

・Title of paper : Simulating Piano Performance Mistakes for Music Learning
・Summary of paper : 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 beginner intermediate piano performances in practice or work-in progress 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.

Comment on receiving this award

The team is delighted and honored to receive this award. We believe the kind of computational model of piano mistakes backed by user studies will be helpful for researchers in this field and for designing systems for helping music learners. We will continue this line of research to support music learning through technology.