Identification of key music symbols for optical music recognition and on-screen presentation

Tambouratzis, Tatiana

doi:10.1109/ijcnn.2011.6033461

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…For this reason, although the majority of the OMR applications perform staff-line segmentation, this is not necessarily always the case. Indeed Tambouratzis [19] adopts the staff lines in the symbol prototypes and performs the symbol recognition directly on the image without first removing staff lines. Others, such as Rossant and Bloch [16] and Toyama et al [21] perform symbol segmentation through correlation and do not require separate staff line removal, while Lee et al [12] and Nguyen and Lee [14] who use vertical projects for symbol segmentation, acknowledge the contribution of the staff lines as a baseline in the vertical projections, but do not remove them.…”

Section: Related Workmentioning

confidence: 99%

“…The pre-processing steps are followed by symbol recognition algorithms which may be applied to the music score using pixel level information or to some higher level function that describes the symbol [15]. At a pixel level, symbol recognition may be applied to primitives such as line segments [21], glyphs that form parts to the musical symbol [13] or the symbol in its entirety [19]. Algorithms such as that described in Lee et al [12], for example, look beyond the pixel level and represent the symbols by their vertical projections, performing the symbol classification on the projections.…”

Section: Related Workmentioning

confidence: 99%

“…There are algorithms which use simple pattern recognition techniques such as template matching [2,21], morphology operations [13] and Hough transform [2]. Other algorithms based on probabilistic approaches [19], convolutional neural networks [11], recurrent neural networks [3] and support vector machines [14] are also described in the literature.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Automatic Ornament Localisation, Recognition and Expression from Music Sheets

Bonnici

Abela

Zammit

et al. 2018

Proceedings of the ACM Symposium on Document Engineering 2018

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Musical notation is a means of passing on performance instructions with fidelity to others. Composers, however, often introduced embellishments to the music they performed notating these embellishments with symbols next to the relevant notes. In time, these symbols, known as ornaments, and their interpretation became standardized such that there are acceptable ways of interpreting an ornament. Although music books may contain footnotes which express the ornament in full notation, these remain cumbersome to read. Ideally, a music student will have the possibility of selecting ornamented notes and express them as full notation. The student should also have the possibility to collapse the expressed ornament back to its symbolic representation, giving the student the possibility of also becoming familiar with playing from the ornamented score. In this paper, we propose a complete pipeline that achieves this goal. We compare the use of cosfire and template matching for optical music recognition to identify and extract musical content from the score. We then express the score using Mu-sicXML and design a simple user interface which allows the user to select ornamented notes, view their expressed notation and decide whether they want to retain the expressed notation, modify it, or revert to the symbolic representation of the ornament. The performance results that we achieve indicate the effectiveness of our proposed approach.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Ornament Localisation, Recognition and Expression from Music Sheets

Bonnici

Abela

Zammit

et al. 2018

Proceedings of the ACM Symposium on Document Engineering 2018

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“…The digital music stand (DMS; Tambouratzis) was introduced as a minimal‐processing alternative to the classical OMR implementations. Targeting the ensemble of amateur and professional musicians as well as of choir singers—in all cases including those with visual impairments—the main objective of the DMS is the online on‐screen custom‐made presentation of a given MS, which—unlike classical OMR systems—is realized without prior MS alignment, noise, and staff line removal and without implementing full music symbol recognition.…”

Section: Introductionmentioning

confidence: 99%

The Digital Music Stand as a Minimal Processing Custom-Made Optical Music Recognition System, Part 1: Key Music Symbol Recognition

Tambouratzis

2013

Int. J. Intell. Syst.

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The digital music stand is proposed as a minimal‐processing optical music recognition implementation, where music score (MS) presentation is realized without prior alignment, noise, or staff line removal. After each MS page is segmented into systems, staves, measures, and candidate music symbols, music symbol recognition is accomplished via probabilistic neural networks: Only the key music symbols (namely clefs, global accidentals, time signatures) of the MS are identified, while the remaining music symbols are generally classified. Subsequently, satisfactory quality of on‐screen MS viewing is accomplished via the concatenation and/or substitution of appropriately selected parts and isolated music symbols of the original MS. In this piece of research, the processing stages leading to on‐screen MS presentation are detailed.

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“…The digital music stand (DMS) was introduced in 2008 [13][14] as an OMR system focusing upon the custom-made on-screen and on-line reconfigurable MM presentation. This aim is accomplished via the minimal possible amount of image (MM page) processing, thus rendering the DMS a representative of the middle ground that lies between full-processing classical commercial systems/platforms and pure MM presentation applications.…”

Section: Introductionmentioning

confidence: 99%

An Implementation of the Digital Music Stand for Custom-Made On-Screen Music Manuscript Viewing

Tambouratzis

Tzormpatzaki

2012

IFIP Advances in Information and Communication Technology

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Abstract. An implementation of the digital music stand (DMS) is presented in this invited tutorial. The DMS constitutes an optical music recognition (OMR) system whose principal aim is the custom-made on-screen presentation of music manuscripts (MMs). Aiming at performing the minimum necessary amount of MM processing, the DMS constitutes middle ground between fullprocessing classical commercial OMR systems/platforms and pure MM presentation systems. The DMS is intended for musicians and choir singers at all levels of music erudition, in both cases including those with visual impairments which cannot be corrected with glasses or contact lenses; music activities ranging from study, rehearsal and performance of individuals as well as of orchestras are accommodated. The DMS implementation stages are detailed, with performance accuracy, which is necessary for correct MM presentation, also being reported.

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Identification of key music symbols for optical music recognition and on-screen presentation

Cited by 6 publications

References 21 publications

Automatic Ornament Localisation, Recognition and Expression from Music Sheets

Automatic Ornament Localisation, Recognition and Expression from Music Sheets

The Digital Music Stand as a Minimal Processing Custom-Made Optical Music Recognition System, Part 1: Key Music Symbol Recognition

An Implementation of the Digital Music Stand for Custom-Made On-Screen Music Manuscript Viewing

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