The acquisition, representation and reconstruction of printed music by computer: A review

Carter, N. P.; Bacon, Richard; Messenger, T.

doi:10.1007/bf00057651

Cited by 13 publications

(5 citation statements)

References 46 publications

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“…Linear Adjacency Graph was patented by N.P.Carter [6], in this method, the input image is first scanned vertically and vertical runs of black pixels are run-length encoded. An individual vertical run of black pixels is called a segment.…”

Section: Detecting Staff-lines Methodsmentioning

confidence: 99%

Detecting the Staff-Lines of Musical Score with Hough Transform and Mathematical Morphology

Chen

Zhang

et al. 2010

2010 International Conference on Multimedia Technology

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This paper presents conventional architecture of Optical Music Recognition (OMR), staff-lines detection is a key stage in OMR. It explains two major methods of staff-lines detection in detail, and it selects 68 images to experiment for the methods. The result of experiment indicates that two methods both can detect all staff-lines of an image by Hough Transform and Mathematical Morphology, but the runtime of Hough Transform is long than Mathematical Morphology.

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Section: Detecting Staff-lines Methodsmentioning

confidence: 99%

Detecting the Staff-Lines of Musical Score with Hough Transform and Mathematical Morphology

Chen

Zhang

et al. 2010

2010 International Conference on Multimedia Technology

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“…The literature acknowledges active research in the 1970's and 1980's, see, for example, the reviews in [2,3], until the first commercial products in the early 1990's. The success of these works relies heavily on available knowledge (as opposed to other document analysis problems): reasonable number of symbols, strict location of the staff lines, strong rules of music writing.…”

Section: Introductionmentioning

confidence: 99%

Robust and Adaptive OMR System Including Fuzzy Modeling, Fusion of Musical Rules, and Possible Error Detection

Rossant

Bloch

2006

EURASIP J. Adv. Signal Process.

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This paper describes a system for optical music recognition (OMR) in case of monophonic typeset scores. After clarifying the difficulties specific to this domain, we propose appropriate solutions at both image analysis level and high-level interpretation. Thus, a recognition and segmentation method is designed, that allows dealing with common printing defects and numerous symbol interconnections. Then, musical rules are modeled and integrated, in order to make a consistent decision. This high-level interpretation step relies on the fuzzy sets and possibility framework, since it allows dealing with symbol variability, flexibility, and imprecision of music rules, and merging all these heterogeneous pieces of information. Other innovative features are the indication of potential errors and the possibility of applying learning procedures, in order to gain in robustness. Experiments conducted on a large data base show that the proposed method constitutes an interesting contribution to OMR.

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“…The literature acknowledges active research in the 1970s and 1980s, see e.g. the reviews in Blostein and Baird [5] and Carter et al [7], until the ÿrst commercial products in the early 1990s. The success of these works relies on the strong available knowledge (as opposed to other document analysis problems): reasonable number of symbols, strict location of the sta lines, strong rules of music writing.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy model for optical recognition of musical scores

Rossant

Bloch

2004

Fuzzy Sets and Systems

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Optical music recognition aims at reading automatically scanned scores in order to convert them in an electronic format, such as a midi ÿle. We only consider here classical monophonic music: we exclude any music written on several staves, but also any music that contains chords. In order to overcome recognition failures due to the lack of methods dealing with structural information, non-local rules and corrections, we propose a recognition approach integrating structural information in the form of relationships between symbols and of musical rules. A nother contribution of this paper is to solve ambiguities by accounting for sources of imprecision and uncertainty, within the fuzzy set and possibility theory framework. We add to a single symbol analysis several rules for checking the consistency of hypotheses: graphical consistency (compatibility between accidental and note, between grace note and note, between note and augmentation dot, etc.), and syntactic consistency (accidentals, tonality, metric). A ll these rules are combined in order to lead to better decisions. Experimental results on 65 music sheets show that our approach leads to very good results, and is able to correct errors made by other approaches, such as the one of SmartScore.

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The acquisition, representation and reconstruction of printed music by computer: A review

Cited by 13 publications

References 46 publications

Detecting the Staff-Lines of Musical Score with Hough Transform and Mathematical Morphology

Detecting the Staff-Lines of Musical Score with Hough Transform and Mathematical Morphology

Robust and Adaptive OMR System Including Fuzzy Modeling, Fusion of Musical Rules, and Possible Error Detection

A fuzzy model for optical recognition of musical scores

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