Optical Music Recognition

2011 ◽  
pp. 80-110 ◽  
Author(s):  
Pierfrancesco Bellini ◽  
Ivan Bruno ◽  
Paolo Nesi
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Research And Development ◽  
European Commission ◽  
Image Understanding ◽  
Development Project ◽  
Music Notation ◽  
Optical Music Recognition ◽  
Digital World ◽  
Music Recognition

Optical music recognition is a key problem for coding western music sheets in the digital world. This problem has been addressed in several manners obtaining suitable results only when simple music constructs are processed. To this end, several different strategies have been followed, to pass from the simple music sheet image to a complete and consistent representation of music notation symbols (symbolic music notation or representation). Typically, image processing, pattern recognition and symbolic reconstruction are the technologies that have to be considered and applied in several manners the architecture of the so called OMR (Optical Music Recognition) systems. In this chapter, the O3MR (Object Oriented Optical Music Recognition) system is presented. It allows producing from the image of a music sheet the symbolic representation and save it in XML format (WEDELMUSIC XML and MUSICXML). The algorithms used in this process are those of the image processing, image segmentation, neural network pattern recognition, and symbolic reconstruction and reasoning. Most of the solutions can be applied in other field of image understanding. The development of the O3MR solution with all its algorithms has been partially supported by the European Commission, in the IMUTUS Research and Development project, while the related music notation editor has been partially funded by the research and development WEDELMUSIC project of the European Commission. The paper also includes a methodology for the assessment of other OMR systems. The set of metrics proposed has been used to assess the quality of results produce by the O3MR with respect the best OMR on market.

An Overview of Optical Music Recognition in China

2011 ◽  
Vol 225-226 ◽  
pp. 223-227
Author(s):  
Gen Fang Chen ◽  
Wen Jun Zhang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Music Education ◽  
Music Theory ◽  
Contemporary Music ◽  
Digital Music ◽  
Musical Score ◽  
Optical Music Recognition ◽  
Research Status ◽  
Music Recognition

OMR (Optical Music Recognition) is a technology for digital musical score image processing and recognition by computer, which has broad applications in the digital music library, contemporary music education, music theory, music automatic classification, music and audio sync dissemination and etc. This paper first has a brief description of OMR research and focuses on describing the research of Chinese OMR literature, it represents the research status and results in China, then the paper pointes out that the target of OMR research in China must tend to Chinese traditional musical score image processing and pattern recognition.

Applying Machine Learning in Optical Music Recognition of Numbered Music Notation

2017 ◽  
Vol 8 (3) ◽  
pp. 21-41 ◽  
Author(s):  
Fu-Hai Frank Wu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Objective Function ◽  
Common Ground ◽  
Recognition Accuracy ◽  
Daily Life ◽  
Sight Reading ◽  
Music Notation ◽  
Optical Music Recognition ◽  
Music Recognition

Although research of optical music recognition (OMR) has existed for few decades, most of efforts were put in step of image processing to approach upmost accuracy and evaluations were not in common ground. And major music notations explored were the conventional western music notations with staff. On contrary, the authors explore the challenges of numbered music notation, which is popular in Asia and used in daily life for sight reading. The authors use different way to improve recognition accuracy by applying elementary image processing with rough tuning and supplementing with methods of machine learning. The major contributions of this work are the architecture of machine learning specified for this task, the dataset, and the evaluation metrics, which indicate the performance of OMR system, provide objective function for machine learning and highlight the challenges of the scores of music with the specified notation.

An Off-Line Optical Music Sheet Recognition

2011 ◽  
pp. 40-77 ◽  
Author(s):  
Pierfrancesco Bellini ◽  
Ivan Bruno ◽  
Paolo Nesi
Keyword(s):  
Recognition System ◽  
Music Notation ◽  
Original Music ◽  
Optical Processing ◽  
Optical Music Recognition ◽  
Digital World ◽  
Music Recognition ◽  
Critical Phase ◽  
Graphic Elements ◽  
Music Symbol

The optical music recognition is a key problem for coding music sheets of western music in the digital world. The most critical phase of the optical music recognition process is the first analysis of the image sheet. In optical processing of music or documents, the first analysis consists of segmenting the acquired sheet into smaller parts in order to recognize the basic symbols that allow reconstructing the original music symbol. In this chapter, an overview of the main issues and a survey of the main related works are discussed. The O3MR system (Object Oriented Optical Music Recognition) system is also described. The used approach in such system is based on the adoption of projections for the extraction of basic symbols that constitute graphic elements of the music notation. Algorithms and a set of examples are also included to better focus concepts and adopted solutions.

Wavelets for Dealing with Super-Imposed Objects in Recognition of Music Notation

2011 ◽  
pp. 78-107
Author(s):  
Susan E. George
Keyword(s):  
Image Processing ◽  
Theoretical Perspective ◽  
Image Filtering ◽  
General Purpose ◽  
Music Notation ◽  
Two Dimensional ◽  
Optical Music Recognition ◽  
Music Recognition ◽  
Wavelet Image

This chapter is concerned with a problem that arises in Optical Music Recognition (OMR) when notes and other music notation symbols are super-imposed upon stavelines in the music image. We investigate a general-purpose, knowledge-free method of image filtering using two-dimensional wavelets to separate the super-imposed objects. Some background is given to the area of wavelets and a demonstration of how stavelines can be located in a wavelet-filtered image. We also explore the separation of foreground objects (notes) from the background (stavelines) over a variety of image resolutions, in binary and greyscale images using a pixel-based truth representation of the image to evaluate the accuracy with which symbols are identified. We find that the Coifmann family of wavelets appear most suitable for vertical image components, and the Daubechies for the horizontal. The motivation for this chapter stems from the desire to (i) make an original wavelet application in image processing, (ii) provide a fresh (theoretical) perspective on the problem of super-imposed objects in music notation, recognizing the duality of the segregation task that exists with staveline removal/symbol extraction and (iii) evaluate how beneficial wavelet image filtering might be to the OMR process.

Applying Machine Learning in Optical Music Recognition of Numbered Music Notation

2020 ◽  
pp. 1915-1937
Author(s):  
Fu-Hai Frank Wu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Objective Function ◽  
Common Ground ◽  
Recognition Accuracy ◽  
Daily Life ◽  
Sight Reading ◽  
Music Notation ◽  
Optical Music Recognition ◽  
Music Recognition

Although research of optical music recognition (OMR) has existed for few decades, most of efforts were put in step of image processing to approach upmost accuracy and evaluations were not in common ground. And major music notations explored were the conventional western music notations with staff. On contrary, the authors explore the challenges of numbered music notation, which is popular in Asia and used in daily life for sight reading. The authors use different way to improve recognition accuracy by applying elementary image processing with rough tuning and supplementing with methods of machine learning. The major contributions of this work are the architecture of machine learning specified for this task, the dataset, and the evaluation metrics, which indicate the performance of OMR system, provide objective function for machine learning and highlight the challenges of the scores of music with the specified notation.

Optical Music Recognition using Image Processing and Machine Learning

2018 ◽  
Vol 06 (10) ◽  
pp. 18-23
Author(s):  
Prince Mathew ◽  
Rahul Vijayakumar ◽  
Aju Tom Kuriakose ◽  
Jesmy Sunny ◽  
Ramani Bai V
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Optical Music Recognition ◽  
Music Recognition

Optical Music Recognition with Wavelets

2005 ◽  
pp. 2225-2229
Author(s):  
Susan E. George
Keyword(s):  
Wavelet Transform ◽  
General Purpose ◽  
Music Notation ◽  
Optical Music Recognition ◽  
Music Recognition ◽  
Logical Representation ◽  
High Level

The aim of optical music recognition (OMR) is to “recognise” images of music notation and capture the “meaning” of the music. When OMR is successful it will be able to automatically extract a logical representation of printed or handwritten music captured in an image. There are a variety of reasons why OMR is required. Chiefly, it is convenient for swift input of music notation and might be subsequently edited, performed, used as a search or other. There are many stages before that final high-level interpretation can be made and recognition of the primitive symbols contained in the notation is primary. One of the biggest challenges in OMR is the super-imposition of music notation symbols – notes and other – upon stave lines in the music image. This article examines a general-purpose knowledge-free method in the wavelet transform, to deal with super-imposition in images of typeset music.

Special Issue on Vision

1999 ◽  
Vol 11 (2) ◽  
pp. 87-87
Author(s):  
Shunichiro Oe ◽  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Pattern Recognition ◽  
Visual Processing ◽  
Research Work ◽  
Image Understanding ◽  
Normal Vector ◽  
Automatic Welding ◽  
Special Issue ◽  
Image Sensing

The widely used term <B>Computer Vision</B> applies to when computers are substituted for human visual information processing. As Real-world objects, except for characters, symbols, figures and photographs created by people, are 3-dimensional (3-D), their two-dimensional (2-D) images obtained by camera are produced by compressing 3-D information to 2-D. Many methods of 2-D image processing and pattern recognition have been developed and widely applied to industrial and medical processing, etc. Research work enabling computers to recognize 3-D objects by 3-D information extracted from 2-D images has been carried out in artificial intelligent robotics. Many techniques have been developed and some applied practically in scene analysis or 3-D measurement. These practical applications are based on image sensing, image processing, pattern recognition, image measurement, extraction of 3-D information, and image understanding. New techniques are constantly appearing. The title of this special issue is <B>Vision</B>, and it features 8 papers from basic computer vision theory to industrial applications. These papers include the following: Kohji Kamejima proposes a method to detect self-similarity in random image fields - the basis of human visual processing. Akio Nagasaka et al. developed a way to identify a real scene in real time using run-length encoding of video feature sequences. This technique will become a basis for active video recording and new robotic machine vision. Toshifumi Honda presents a method for visual inspection of solder joint by 3-D image analysis - a very important issue in the inspection of printed circuit boards. Saburo Okada et al. contribute a new technique on simultaneous measurement of shape and normal vector for specular objects. These methods are all useful for obtaining 3-D information. Masato Nakajima presents a human face identification method for security monitoring using 3-D gray-level information. Kenji Terada et al. propose a method of automatic counting passing people using image sensing. These two technologies are very useful in access control. Yoji. Ogawa presents a new image processing method for automatic welding in turbid water under a non-preparatory environment. Liu Wei et al. develop a method for detection and management of cutting-tool wear using visual sensors. We are certain that all of these papers will contribute greatly to the development of vision systems in robotics and mechatronics.

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