How computers translate visual musical scores into digital data for analysis
In short: This MIT lecture explores the intersection of music theory and computational analysis. It examines the technical challenges of optical music recognition and the methods used to correct errors when converting images of scores into machine-readable data.
Part of the Spring 2023 course Computational Music Theory and Analysis, this session led by instructor Michael Scott Asato Cuthbert investigates the complexities of optical music recognition (OMR). The lecture addresses how music theory knowledge can be applied to identify and rectify inaccuracies that occur during the digitization process.
Beyond recognition software, the material covers the analysis of gap fill in species counterpoint and other musical corpora. By understanding the limitations of current image-to-score technology, researchers can better optimize workflows for large-scale musical data analysis.
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Sources
- Summary by Intellect · source: Class 31 Video: Music Visualization and Optical Music Recognition
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