Music Manuscript Reconstruction

I have been working on methods for recovering music notation from degraded or damaged manuscripts, starting earlier this year. A first step in this project was developing some theory around an image quality and contrast measure at [1].

I am fortunate to collaboration on this work with Dr. Anna Breger and many other partners. I coauthored the grant from the Cambridge Centre for Data-Driven Discovery and Accelerate Programme for Scientific Discovery (made possible by a donation from Schmidt Sciences, with Dr. Anna Breger and Prof. Carola-Bibiane Schönlieb as PIs) titled AI meets cultural heritage: Non-invasive imaging and machine learning techniques for the reconstruction of degraded historical sheet music. From the project announcement

This project will explore the possibilities, challenges and limitations of imaging and machine learning methods for reconstructing degraded historical sheet music. Such degradations may happen due to chemical or physical damage. The project team will form a novel collaboration network spanning libraries, imaging laboratories and AI imaging researchers. Samples will be selected of musical manuscripts of historical interest and the team will employ advanced imaging systems to scan these manuscripts and apply standard as well as newly developed, advanced machine learning methods to reconstruct the degraded parts.