Distributed Digital Music Archives and Libraries (DDMAL)


Gamut for Early Music on Microfilms (GEMM) project

Unlike text databases, online content-searchable databases of music scores are extremely rare. The main reasons are the cost of digitization, the inaccessibility of original music scores and manuscripts, and the lack of sophisticated music recognition software. The proposed research will attempt to circumvent these problems by investigating the feasibility of using existing microfilms for digitization.
Compared to original scores, microfilms constitute more accessible and economical sources for digitization (there are over 7000 music scores in microfilm format in North America). Scanned images of music scores can be made content searchable through the use of optical music recognition (OMR) software developed by the PI (Fujinaga 1997).

The objective of this study is to determine whether the quality of images scanned from microfilm, rather than from the original score, is sufficient for the subsequent OMR process. If the digital image derived from microfilm is found to be acceptable for OMR, there will be tremendous economic benefits. The cost of building digital libraries is incurred mainly in the digitization process. The cost of digitization from microfilm is far lower than that from paper. Using microfilms may also prove highly advantageous in data collection. Original music manuscripts are scattered throughout the world in various libraries, museums, and archives. The exorbitant cost of traveling to these locations can be avoided through the use of microfilm collections that already exist in many music libraries. Finally, when a local archive decides to digitize its own collection, using available microfilms will obviate the necessity of handling and thereby potentially damaging precious manuscripts.

With the infrastructure, it will be possible to study the feasibility of using microfilm by comparing the image quality of direct scan and microfilm scan of the same music scores. The requested high-quality flatbed scanner (Epson 1640XL) and a microfilm scanner (Minolta MS6000) with greyscale option are necessary for the experiment. The principal metric is the recognition rate of the OMR process, although visual inspection of the image may be sufficient in some cases. Various types of music scores representative of different historical periods and microfilms produced from different sources and of varying quality will be studied. The project will concentrate on music from Medieval and Renaissance periods (including lute and guitar tablatures), because access to original sources from these periods is particularly difficult and the software must be trained to recognize notation systems that differ from the common music notation system in current use. A sample of different notation styles with varying degree of print quality will be examined. It should be noted that no other OMR software is capable of handling the types of music notations specific to Medieval and Renaissance music.

The current OMR system is integrated into a document analysis system called Gamera (MacMillan, Droettboom, and Fujinaga. 2002), which has been used in the Levy Sheet Music Project at the Johns Hopkins University (Choudhury et al. 2001). The unique feature of Gamera is its ability to learn new symbols through exemplar-based learning algorithms enhanced by the use of genetic algorithms.

If the quality of images scanned from microfilm is sufficient to obtain reasonable results using the OMR process, a large amount of music can be made searchable, creating an incredible resource for music scholars throughout the world.


2005/04: This project is funded by SSHRC Standard Research Grant “Feasibility of Digitizing Early Music on Microfilms for the Creation of Large-scale Content-searchable Database” (2005–8) $145,838.



  • John Ashley Borgoyne (McGill)
  • Remi Chou (McGill)
  • Julie Cumming (McGill)
  • Cory McKay (McGill)
  • Laurent Pugin (Université de Genève)
  • Susan Weiss (Johns Hopkins)


Choudhury, G. S., T. DiLauro, M. Droettboom, I. Fujinaga, and K. MacMillan. 2001. Strike up the score: Deriving searchable and playable digital formats from sheet music. D-Lib Magazine 7 (2).

Fujinaga, I. 1997. Adaptive optical music recognition. Ph.D. Dissertation. McGill University.

MacMillan, K, M. Droettboom, and I. Fujinaga. 2002. Gamera: Optical music recognition in a new shell. Proceedings of the International Computer Music Conference. 482-5.

Created: 2004.04.10 Modified: Ichiro Fujinaga
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