Taking advantage of recent AI and online education technology, new tools assist students in learning the violin, broadening access for all.

Speaker: CORNELIA FERMÜLLER, Research Scientist, Institute for Advanced Computer Studies, University of Maryland

Date: October 4, 2024

Time: 9:00 AM PT | 11:00 AM CT | 12:00 PM ET | 18:00 h CET

Abstract: Music is an integral part of our lives, and there is evidence that instrumental education has benefits on the development of students’ cognitive, social, and physical skills. However, learning to play a musical instrument constitutes a lengthy and complex process, and there are challenges in keeping students engaged. Taking advantage of recent AI and online education technology, we have been developing tools to assist violin students in learning the violin. These include audio-visual analysis to give feedback on incorrect posture, assistance with acquiring good bowing, a haptic device to help teach rhythmic skills, and individualized suggestions for practice materials. The work is expected to advance music pedagogy and and provide the benefits of instrumental instruction to a large and more diverse population.

BIO: Cornelia Fermüller is the head of the ASciNA Washington DC chapter. She is a research scientist at the Institute for Advanced Computer Studies (UMIACS) at the University of Maryland at College Park. She holds a Ph.D. from the Technical University of Vienna, Austria, and an M.S. from the University of Technology, Graz, Austria, both in Applied Mathematics. She co-founded the Autonomy Cognition and Robotics (ARC) Lab and co-leads UMD’s Perception and Robotics Group. She is the PI of an NSF-sponsored Network for Accelerating Research on Neuromorphic Engineering. Her research is in Computer, Human, and Robot Vision. She studies and develops biologically inspired Computer Vision solutions for systems interacting with their environment. In recent years, her work has focused on interpreting human activities and motion processing for fast, active robots using bio-inspired event-based sensors as input.

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