Low- and high-level approaches to optical music score recognition

The computer has become an increasingly important device in music. It can not only generate sound but is also able to perform time consuming and repetitive tasks, such as transposition and part extraction, with speed and accuracy. However, a score must be represented in a machine readable format before any operation can be carried out. Current input methods, such as using an electronic keyboard, are time consuming and require human intervention. Optical music recognition (OMR) provides an interesting, efficient and automatic method to transform paper-based music scores into a machine representation. The authors outline the techniques for pre-processing and discuss the heuristic and musical rules employed to enhance recognition. A spin-off application that makes use of the intermediate results to enhance stave lines is also presented. The authors concentrate on the techniques used for time-signature detection, discuss the application of frequently-found rhythmical patterns to clarify the results of OMR, and propose possible enhancements using such knowledge. They believe that domain-knowledge enhancement is essential for complex document analysis and recognition. Other possible areas of development include melodic, harmonic and stylistic analysis to improve recognition results further.