Towards a digital library of popular music
Proceedings of the fourth ACM conference on Digital libraries
PROLOG Programming for Artificial Intelligence
PROLOG Programming for Artificial Intelligence
Structured Document Image Analysis
Structured Document Image Analysis
Handbook of AI
A simplified attributed graph grammar for high-level music recognition
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 2) - Volume 2
Automatic Computer Recognition of Printed Music
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
DCC '98 Proceedings of the Conference on Data Compression
Adaptive optical music recognition
Adaptive optical music recognition
Assessing Optical Music Recognition Tools
Computer Music Journal
EURASIP Journal on Applied Signal Processing
[COMSCAN]: an optical music recognition system
Proceedings of the 7th International Conference on Frontiers of Information Technology
Note symbol recognition for music scores
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part II
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Optical music recognition (OMR) systems are used to convert music scanned from paper into a format suitable for playing or editing on a computer. These systems generally have two phases: recognizing the graphical symbols (such as note-heads and lines) and determining the musical meaning and relationshipsof the symbols (such as the pitch and rhythm of the notes). In this paper we explore the second phase and give a two-step approach that admits an economical representation of the parsing rules for the system. The approach is flexible and allows the system to be extended to new notations with little effort--the current system can parse common music notation, Sacred Harp notation and plainsong. It is based on a string grammar and a customizable graph that specifies relationships between musical objects. We observe that this graph can be related to printing as well as recognizing music notation, bringing the opportunity for cross-fertilization between the two areas of research.