Enhancing Online Music Lessons with Applications in Automating Self-Learning Tutorials and Performance Assessment

Online music lessons are growing more and more prevalent with resources such as Massive Open Online Courses (MOOCs) and lessons via YouTube videos. We discuss two issues in online music lessons that machine learning techniques may be used to solve: finding or developing a music lesson based on the student´s learning style, musical background, or preference and quantitative assessment of the student´s performance (whether as an online-instructor or self-taught through online videos). In particular, we discuss two solutions with specific applications. First, we propose a method for automating music lessons for learning how to play pre-recorded songs via existing music information retrieval (MIR) techniques for adaptability to various learning styles. Secondly, we propose a method for automating the assessment of rhythmic structures (such as tremolo, vibrato, etc.) via quantitative metrics of comparing modulation spectral features. We then list some resources and software that are currently available to integrate these methods to enhance online music education.