AI Empowered Music Education
IRB 4105
Learning a musical instrument is a complex process involving years of practice and feedback. However, dropout rates in music programs, particularly among violin students, remain high due to socio-economic barriers and the challenge of mastering the instrument. My dissertation explores the feasibility of accelerating learning and leveraging technology in music education, with a focus on bowed string instruments, specifically the violin. My research identifies workflow gaps and challenges for the stakeholders, aiming to address not only the improvement of learning outcomes but also the provision of opportunities for socioeconomically challenged students. Three key areas are emphasized: designing user studies and creating a comprehensive violin dataset, developing tools and deep learning algorithms for accurate performance assessment, and crafting a practice platform for student feedback. These efforts seek to democratize access to quality music education and address dropout rates in music programs.
Examining Committee
Chair:
Dr. John Aloimonos
Dean's Representative:
Dr. Irina Muresanu
Members:
Dr. Cornelia Fermuller
Dr. Ge Gao
Dr. Ramani Duraiswami