HMM-based writer identification in music score documents without staff-line removal. (15th December 2017)

• Record Type: Journal Article
• Authors: Roy, Partha Pratim; Bhunia, Ayan Kumar; Pal, Umapada
• Is Part Of: Expert systems with applications. Volume 89(2017)
• Page Start: 222
• Page End: 240
• Publication Date: 2017-12-15
• Subjects: Music score documents -- Writer identification -- Hidden Markov model -- Factor analysis; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.07.031
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A writer identification system in musical documents without removing staff-lines. An improved feature selection method for better writer identification performance. An efficient musical score-line segmentation using HMM is proposed. The system has been tested for writer identification in historical musical documents. 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.