A directed acyclic graph-large margin distribution machine model for music symbol classification
Date
2016-03-17
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Advisor
Coadvisor
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Publisher
Mansour Ebrahimi, Qom University, ISLAMIC REPUBLIC OF IRAN
Language
English
Alternative Title
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).
Keywords
Support vector machines, Bioacoustics, Algorithms, Kernel functions, Deformation, Hidden Markov models, Neural Networks
Document Type
Journal article
Publisher Version
doi.org/10.1371/journal.pone.0149688
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Citation
Wen, C., Zhang, J., Rebelo, A., & Cheng, F. (2016). A directed acyclic graph-large margin distribution machine model for music symbol classification. PLoS ONE, 11(3), 1-12. https://doi.org/10.1371/journal.pone.0149688. Disponível no Repositório UPT, http://hdl.handle.net/11328/2470
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Access Type
Open Access