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.

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

AN OPTICAL MUSIC RECOGNITION SYSTEM FOR SKEW OR INVERTED MUSICAL SCORES

2013 ◽  
Vol 27 (07) ◽  
pp. 1353005 ◽  
Author(s):  
YUNG-SHENG CHEN ◽  
FENG-SHENG CHEN ◽  
CHIN-HUNG TENG
Keyword(s):  
Image Processing ◽  
Recognition Accuracy ◽  
Recognition System ◽  
Pop Music ◽  
Musical Score ◽  
Optical Music Recognition ◽  
Pattern Recognition Techniques ◽  
Musical Scores ◽  
Music Recognition ◽  
Series Of Experiments

Optical Music Recognition (OMR) is a technique for converting printed musical documents into computer readable formats. In this paper, we present a simple OMR system that can perform well for ordinary musical documents such as ballad and pop music. This system is constructed based on fundamental image processing and pattern recognition techniques, thus it is easy to implement. Moreover, this system has a strong capability in skew restoration and inverted musical score detection. From a series of experiments, the error for our skew restoration is below 0.2° for any possible document rotation and the accuracy of inverted musical score detection is up to 98.89%. The overall recognition accuracy of our OMR can achieve to nearly 97%, a figure comparable with current commercial OMR software. However, if taking into image skew into consideration, our system is superior to commercial software in terms of recognition accuracy.

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.

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.

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.

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.

scholarly journals Selected Research in Computer Music

2017 ◽  
Author(s):  
Michael Droettboom
Keyword(s):  
Signal Processing ◽  
Digital Signal Processing ◽  
Programming Language ◽  
Video Processing ◽  
Motion Tracking ◽  
Digital Signal ◽  
Music Notation ◽  
Optical Music Recognition ◽  
Music Interpretation ◽  
Music Recognition

This thesis describes three interrelated projects that cut across the author’s interests in musical information representation and retrieval, programming language theory, machine learning and human/computerinteraction.I. Optical music recognition. This first part introduces an optical music interpretation (OMI) system that derives musical information from the symbols on sheet music.The first chapter is an introduction to OMI’s parent field of optical music recognition (OMR), and to the present implementation as created for the Levy project. It is important that OMI has a representation standard in which to create its output. Therefore, the second chapter is a somewhat tangential but necessary study of computer-based musical representation languages, with particular emphasis on GUIDO and Mudela. The third and core chapter describes the processes involved in the present optical music interpretation system. While there are some details related to its implementation in the Python programming language, most of the material involves issues surrounding music notation rather thancomputer programming. The fourth chapter demonstrates how the logical musical data generated by the OMI system can be used as part of a musical search engine.II. Tempo extraction. The second part presents a system to automatically obtain the tempo and rubato curves from recorded performances, by aligning them to strict-tempo MIDI renderings of the same piece of music. The usefulness of such a system in the context of current musicological research is explored.III. Realtime digital signal processing programming environment. Lastly, a portable and flexible system for realtime digital signal processing (DSP) is presented. This system is both easy-to-use and powerful, in large part because it takes advantage of existing mature technologies. This framework provides the foundation for easier experimentation in new directions in audio and video processing, including physical modeling and motion tracking.

scholarly journals Digital Image Processing Using Machine Learning

2019 ◽  
pp. 125-128
Author(s):  
Priya Bansal ◽  
Mrs. Mamta
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Daily Life ◽  
Pictorial Information ◽  
Important Data ◽  
Information Description

Main aim of Digital Image Processing Using Machine Learning is to extract important data from images. Using this extracted information description, interpretation and understanding of the scene can be provided by the machine. Main point of image processing is to modify images in to desired manner. Image processing is called as altering and analyzing pictorial information of images. In our daily life we come across different type of image processing best example of image processing in our daily life is our brain sensing lot of images when we see images with eyes and processing is done is very less time.

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