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dc.contributor.authorPratim Roy, Partha-
dc.contributor.authorBhunia A.K.-
dc.contributor.authorPal U.-
dc.date.accessioned2020-10-06T15:56:54Z-
dc.date.available2020-10-06T15:56:54Z-
dc.date.issued2017-
dc.identifier.citationExpert Systems with Applications (2017), 89(): 222-240-
dc.identifier.issn9574174-
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2017.07.031-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/5638-
dc.description.abstractWriter 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-
dc.language.isoen_US-
dc.publisherElsevier Ltd-
dc.relation.ispartofExpert Systems with Applications-
dc.subjectFactor analysis-
dc.subjectHidden Markov model-
dc.subjectMusic score documents-
dc.subjectWriter identification-
dc.titleHMM-based writer identification in music score documents without staff-line removal-
dc.typeArticle-
dc.scopusid56880478500-
dc.scopusid57188719920-
dc.scopusid57200742116-
dc.affiliationRoy, P.P., Department of CSE, Indian Institute of Technology Roorkee, India-
dc.affiliationBhunia, A.K., Department of ECE, Institute of Engineering & Management, Kolkata, India-
dc.affiliationPal, U., CVPR Unit, Indian Statistical Institute, Kolkata, India-
dc.description.correspondingauthorRoy, 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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