Master’s Thesis Presentation • Human-Computer Interaction • Technology Design Recommendations Informed by Observations of Videos of Popular Musicians Teaching and Learning Songs By Ear

Please note: This master’s thesis presentation will take place in DC 3317.

Christopher Liscio, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Dan Brown

Instrumentalists who play popular music often learn songs by ear, using recordings in lieu of sheet music or tablature. This practice was made possible by technology that allows musicians to control playback events. Until now, researchers have not studied the human-recording interactions of musicians attempting to learn pop songs by ear.

Through a pair of studies analyzing the content of online videos from YouTube, we generate hypotheses and seek a better understanding of by-ear learning from a recording. Combined with results from neuroscience studies of tonal working memory and aural imagery, our findings reveal a model of by-ear learning that highlights note-finding as a core activity. Using what we learned, we discuss opportunities for designers to create a set of novel human-recording interactions, and to provide assistive technology for those who lack the baseline skills to engage in the foundational note-finding activity.