Learning to play a musical instrument is a long and difficult endeavor. Not everyone can afford the help of a professional teacher, and even with this help, feedback is limited in terms of latency and expressiveness. To tackle these problems, we will design a collection of new data-driven techniques and tools. The main idea is to systematically record musical practice data of students and feed it back through smart, visual interfaces. With a Visual Analytics web tool, we will allow students, teachers, and professional musicians to detect errors and improve their style in a completely new way. By additionally recording motion data, we will also be able to convey fingering instructions or correct poses through Augmented Reality displays that visualize information directly attached to a physical music instrument.
As musical data can be complex and notes or audio signals recorded from instruments are usually noisy, AI is a useful, if not necessary vehicle for data processing and analysis. We will follow a human-centered design process that involves musicians and music teachers of different backgrounds and skill levels in data acquisition, development, and evaluation of our techniques and tools. Our goal is to provide ready-to-use music education tools, re-usable data processing techniques, and datasets comprised of notes, audio, motion capturing, and other features that we record from instruments and players.