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

TL;DR

Soloist is a framework that transforms existing guitar instructional videos into interactive, customizable learning experiences by extracting musical information through audio processing, providing real-time feedback and improved navigation for learners.

Contribution

It introduces a novel audio processing method to convert standard instructional videos into interactive tutorials with real-time feedback and tailored curriculums for guitar learners.

Findings

Users preferred Soloist over traditional videos

Soloist enables real-time feedback during guitar practice

The system improves navigation and engagement in learning

Abstract

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.