Domain adaptation for staff-region retrieval of music score images

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...

Full description

Saved in:
Bibliographic Details
Published in:International journal on document analysis and recognition 2022-12, Vol.25 (4), p.281-292
Main Authors: Castellanos, Francisco J., Gallego, Antonio Javier, Calvo-Zaragoza, Jorge, Fujinaga, Ichiro
Format: Article
Language:English
Subjects:
Adaptation
Annotations
Computer Science
Domains
Image Processing and Computer Vision
Musical scores
Neural networks
Object recognition
Pattern Recognition
Retrieval
Special Issue Paper
Workflow
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1433-2833
1433-2825
DOI:10.1007/s10032-022-00411-w