Reducing costs for digitising early music with dynamic adaptation

  • Authors:

  • Laurent Pugin;John Ashley Burgoyne;Ichiro Fujinaga

  • Affiliations:

  • Centre for Interdisciplinary Research in Music and Media Technology, Schulich School of Music of McGill University, Montréal, Québec, Canada;Centre for Interdisciplinary Research in Music and Media Technology, Schulich School of Music of McGill University, Montréal, Québec, Canada;Centre for Interdisciplinary Research in Music and Media Technology, Schulich School of Music of McGill University, Montréal, Québec, Canada

  • Venue:
  • ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

Optical music recognition (OMR) enables librarians to digitise early music sources on a large scale. The cost of expert human labour to correct automatic recognition errors dominates the cost of such projects. To reduce the number of recognition errors in the OMR process, we present an innovative approach to adapt the system dynamically, taking advantage of the human editing work that is part of any digitisation project. The corrected data are used to perform MAP adaptation, a machine-learning technique used previously in speech recognition and optical character recognition (OCR). Our experiments show that this technique can reduce editing costs by more than half.