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André Tabone1, Alexandra Bonnici1, Stefania Cristina1

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Summary

Musicians can now turn digital sheet music pages hands-free using an eye-gaze tracking system. This innovative technology predicts gaze to ensure accurate page turns, improving the digital music reading experience.

Keywords:
Kalman filtereye-gaze trackingeye-hand spanhalf-page turnspage-turning

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Area of Science:

  • Human-Computer Interaction
  • Music Technology
  • Assistive Technology

Background:

  • Musicians increasingly use tablets for digital sheet music, facing challenges with limited screen size and manual page turning.
  • Manual scrolling or tapping disrupts musical flow and requires hands off instruments.
  • Existing eye-gaze tracking systems can be unreliable due to musicians looking away from the screen.

Purpose of the Study:

  • To develop and evaluate an automated, hands-free page-turning system for musicians using tablet devices.
  • To improve the user experience of digital sheet music by addressing the limitations of manual page turning.
  • To create a gaze prediction model to enhance the accuracy and reliability of eye-gaze controlled systems.

Main Methods:

  • Developed a page-turning system that monitors the musician's point of regard on the tablet screen.
  • Implemented a gaze prediction model utilizing Kalman filtering to anticipate the musician's visual focus.
  • Evaluated the system's performance with 15 piano songs of varying difficulty, incorporating repeats and different playing registers.

Main Results:

  • The hands-free page-turning system achieved a 98.3% success rate in executing page turns.
  • A mere 1.7% of page turns were delayed, with no mistaken page turns recorded during the evaluation.
  • The gaze prediction model effectively compensated for instances where the musician's eyes were temporarily outside the tracker's field of view.

Conclusions:

  • The developed eye-gaze controlled page-turning system offers a viable and highly accurate hands-free solution for musicians using digital sheet music.
  • The integration of gaze prediction significantly enhances the robustness and usability of eye-gaze interaction in dynamic performance environments.
  • This technology has the potential to revolutionize how musicians interact with digital scores, enabling a more seamless and immersive performance experience.