Machine-extracted eye gaze features: how well do they correlate to sight-reading abilities of piano players?

Abstract

Skilled piano players are able to decipher and play a musical piece they had never seen before (a skill known as sight-reading). For a sample of 23 piano players of various abilities we consider the correlation between machine-extracted gaze path features and the overall human rating. We find that correlation values (between machine-extracted gaze features and overall human ratings) are statistically similar to correlation values between human-extracted task-related ratings (e.g., note accuracy, error rate) and overall human ratings. These high correlation values suggest that an eye tracking-enabled computer could help students assess their sight-reading abilities, and could possibly advise students on how to improve. The approach could be extended to any musical instrument. For keyboard players, a MIDI keyboard with the appropriate software to provide information about note accuracy and timing could complement feedback from an eye tracker to enable more detailed analysis and advice.