Optical music recognition: state-of-the-art and open issues

• Type: Article in International Scientific Journal
• Date: 2012

Authors

Ana Rebelo (Author) Other; Ichiro Fujinaga (Author) Other; Filipe Paszkiewicz (Author) Other; André Ribeiro Marçal (Author) FCUP; Carlos Guedes (Author) Other; Jaime Cardoso (Author) FEUP

Journal

Title: International Journal of Multimedia Information Retrieval

Vol. 1

Pages: 173-190

ISSN: 2192-6611

Publisher: Springer Nature

Indexing

Scopus - 180 Citations

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Technological sciences > Engineering > Knowledge engineering

Other information

Authenticus ID: P-00H-5KZ

DOI: 10.1007/s13735-012-0004-6

Abstract (EN): For centuries, music has been shared and remembered by two traditions: aural transmission and in the form of written documents normally called musical scores. Many of these scores exist in the form of unpublished manuscripts and hence they are in danger of being lost through the normal ravages of time. To preserve the music some form of typesetting or, ideally, a computer system that can automatically decode the symbolic images and create new scores is required. Programs analogous to optical character recognition systems called optical music recognition (OMR) systems have been under intensive development for many years. However, the results to date are far from ideal. Each of the proposed methods emphasizes different properties and therefore makes it difficult to effectively evaluate its competitive advantages. This article provides an overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores. For self-containment and for the benefit of the reader, an introduction to OMR processing systems precedes the literature overview. The following study presents a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones. © 2012, Springer-Verlag London Limited.

Language: English

Type (Professor's evaluation): Scientific