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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/5638
Title: HMM-based writer identification in music score documents without staff-line removal
Authors: Pratim Roy, Partha
Bhunia A.K.
Pal U.
Published in: Expert Systems with Applications
Abstract: Writer identification from musical score documents is a challenging task due to its inherent problem of overlapping of musical symbols with staff-lines. Most of the existing works in the literature of writer identification in musical score documents were performed after a pre-processing stage of staff-lines removal. In this paper we propose a novel writer identification framework in musical score documents without removing staff-lines from the documents. In our approach, Hidden Markov Model (HMM) has been used to model the writing style of the writers without removing staff-lines. The sliding window features are extracted from musical score-lines and they are used to build writer specific HMM models. Given a query musical sheet, writer specific confidence for each musical line is returned by each writer specific model using a log-likelihood score. Next, a log-likelihood score in page level is computed by weighted combination of these scores from the corresponding line images of the page. A novel Factor Analysis-based feature selection technique is applied in sliding window features to reduce the noise appearing from staff-lines which proves efficiency in writer identification performance. In our framework we have also proposed a novel score-line detection approach in musical sheet using HMM. The experiment has been performed in CVC-MUSCIMA data set and the results obtained show that the proposed approach is efficient for score-line detection and writer identification without removing staff-lines. To get the idea of computation time of our method, detail analysis of execution time is also provided. © 2017 Elsevier Ltd
Citation: Expert Systems with Applications (2017), 89(): 222-240
URI: https://doi.org/10.1016/j.eswa.2017.07.031
http://repository.iitr.ac.in/handle/123456789/5638
Issue Date: 2017
Publisher: Elsevier Ltd
Keywords: Factor analysis
Hidden Markov model
Music score documents
Writer identification
ISSN: 9574174
Author Scopus IDs: 56880478500
57188719920
57200742116
Author Affiliations: Roy, P.P., Department of CSE, Indian Institute of Technology Roorkee, India
Bhunia, A.K., Department of ECE, Institute of Engineering & Management, Kolkata, India
Pal, U., CVPR Unit, Indian Statistical Institute, Kolkata, India
Corresponding Author: Roy, P.P.; Department of CSE, Indian Institute of Technology RoorkeeIndia; email: proy.fcs@iitr.ac.in
Appears in Collections:Journal Publications [CS]

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