AI Empowered Music Education

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