Abstract
Optical Music Recognition (OMR) has received increasing attention in recent years. In this paper, we propose a classifier based on a new method named Directed Acyclic Graph-Large margin Distribution Machine (DAG-LDM). The DAG-LDM is an improvement of the Large margin Distribution Machine (LDM), which is a binary classifier that optimizes the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. We modify the LDM to the DAG-LDM to solve the multi-class music symbol classification problem. Tests are conducted on more than 10000 music symbol images, obtained from handwritten and printed images of music scores. The proposed method provides superior classification capability and achieves much higher classification accuracy than the state-of-the-art algorithms such as Support Vector Machines (SVMs) and Neural Networks (NNs).
Highlights
Musical scores have been preserved in libraries and museums and made available as original manuscripts or scanned copies
We extend the Large margin Distribution Machine (LDM) to Directed Acyclic GraphLarge margin Distribution Machine (DAG-LDM) for the music symbol classification
The handwritten and printed music symbols are randomly split into training and test sets
Summary
Musical scores have been preserved in libraries and museums and made available as original manuscripts or scanned copies. The propagation and availability of such musical sources are limited by the storage methods. With the development of scanning and pattern recognition technology, digital libraries have become increasingly popular. Over the last few years, a growing amount of information has been obtained from digital libraries or the internet. The transformation from traditional music sheets to a machine readable format is essential. Many efforts have been devoted to the development of OMR systems. The currently available music recognition methods are far from satisfactory
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