Fully Automatic Page Turning on Real Scores
Florian Henkel, Stephanie Schwaiger, Gerhard Widmer

TL;DR
This paper introduces a fully automatic page turning system for sheet music that uses a multi-modal neural network to analyze sheet images and live performances, enabling seamless, real-time page turning without symbolic music representations.
Contribution
The novel system combines visual sheet music analysis with audio input to automatically predict page turns, demonstrated through a prototype that physically turns pages in real time.
Findings
Effective real-time page prediction from sheet images and audio
Successful integration with a physical page-turning device
Potential for hands-free music performance support
Abstract
We present a prototype of an automatic page turning system that works directly on real scores, i.e., sheet images, without any symbolic representation. Our system is based on a multi-modal neural network architecture that observes a complete sheet image page as input, listens to an incoming musical performance, and predicts the corresponding position in the image. Using the position estimation of our system, we use a simple heuristic to trigger a page turning event once a certain location within the sheet image is reached. As a proof of concept we further combine our system with an actual machine that will physically turn the page on command.
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Taxonomy
TopicsMusic and Audio Processing · Music Technology and Sound Studies · Video Analysis and Summarization
