Soloist: Generating Mixed-Initiative Tutorials from Existing Guitar Instructional Videos Through Audio Processing


Learning musical instruments using online instructional videos has become increasingly prevalent. However, pre-recorded videos lack the instantaneous feedback and personal tailoring that human tutors provide. In addition, existing video navigations are not optimized for instrument learning, making the learning experience encumbered. Guided by our formative interviews with guitar players and prior literature, we designed Soloist, a mixed-initiative learning framework that automatically generates customizable curriculums from off-the-shelf guitar video lessons. Soloist takes raw videos as input and leverages deep-learning based audio processing to extract musical information. This back-end processing is used to provide an interactive visualization to support effective video navigation and real-time feedback on the user’s performance, creating a guided learning experience. We demonstrate the capabilities and specific use-cases of Soloist within the domain of learning electric guitar solos using instructional YouTube videos. A remote user study, conducted to gather feedback from guitar players, shows encouraging results as the users unanimously preferred learning with Soloist over unconverted instructional videos.

Bryan Wang
University of Toronto, Toronto, Ontario, Canada
Mengyu Yang
University of Toronto, Toronto, Ontario, Canada
Tovi Grossman
University of Toronto, Toronto, Ontario, Canada




会議: CHI 2021

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Engineering Interactive Applications

[B] Paper Room 05, 2021-05-14 01:00:00~2021-05-14 03:00:00 / [C] Paper Room 05, 2021-05-14 09:00:00~2021-05-14 11:00:00 / [A] Paper Room 05, 2021-05-13 17:00:00~2021-05-13 19:00:00
Paper Room 05
14 件の発表
2021-05-14 01:00:00
2021-05-14 03:00:00