Domain adaptation for staff-region retrieval of music score images

Abstract

Optical music recognition (OMR) is the field that studies how to automatically read music notation from score images. One of the relevant steps within the OMR workflow is the staff-region retrieval. This process is a key step because any undetected staff will not be processed by the subsequent steps. This task has previously been addressed as a supervised learning problem in the literature; however, ground-truth data are not always available, so each new manuscript requires a preliminary manual annotation. This situation is one of the main bottlenecks in OMR, because of the countless number of existing manuscripts , and the associated manual labeling cost. With the aim of mitigating this issue, we propose the application of a domain adaptation technique, the so-called Domain-Adversarial Neural Network (DANN), based on a combination of a gradient reversal layer and a domain classifier in the inference neural architecture. The results from our experiments support the benefits of our proposed solution, obtaining improvements of approximately 29% in the F-score.