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Proceedings Paper

Optical music recognition system which learns
Author(s): Ichiro Fujinaga
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Paper Abstract

This paper describes an optical music recognition system composed of a database and three interdependent processes: a recognizer, an editor, and a learner. Given a scanned image of a musical score, the recognizer locates, separates, and classifies symbols into musically meaningful categories. This classification is based on the k-nearest neighbor method using a subset of the database that contains features of symbols classified in previous recognition sessions. Output of the recognizer is corrected by a musically trained human operator using a music notation editor. The editor provides both visual and high-quality audio feedback of the output. Editorial corrections made by the operator are passed to the learner which then adds the newly acquired data to the database. The learner's main task, however, involves selecting a subset of the database and reweighing the importance of the features to improve accuracy and speed for subsequent sessions. Good preliminary results have been obtained with everything from professionally engraved scores to hand-written manuscripts.

Paper Details

Date Published: 20 January 1993
PDF: 8 pages
Proc. SPIE 1785, Enabling Technologies for High-Bandwidth Applications, (20 January 1993); doi: 10.1117/12.139262
Show Author Affiliations
Ichiro Fujinaga, McGill Univ. (Canada)

Published in SPIE Proceedings Vol. 1785:
Enabling Technologies for High-Bandwidth Applications
Jacek Maitan, Editor(s)

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