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.

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 Imaging

2011 ◽  
pp. 50-79 ◽  
Author(s):  
Graham Jones ◽  
Bee Ong ◽  
Ivan Bruno ◽  
Kia NG
Keyword(s):  
Image Restoration ◽  
Imaging Techniques ◽  
Recognition System ◽  
Optical Music Recognition ◽  
Cross Media ◽  
Musical Scores ◽  
Music Recognition ◽  
Potential Applications ◽  
Music Manuscripts ◽  
Media Integration

This paper presents the applications and practices in the domain of music imaging for musical scores (music sheets and music manuscripts), which include music sheet digitisation, optical music recognition (OMR) and optical music restoration. With a general background of Optical Music Recognition (OMR), the paper discusses typical obstacles in this domain and reports currently available commercial OMR software. It reports hardware and software related to music imaging, discussed the SharpEye optical music recognition system and provides an evaluation of a number of OMR systems. Besides the main focus on the transformation from images of music scores to symbolic format, this paper also discusses optical music image restoration and the application of music imaging techniques for graphical preservation and potential applications for cross-media integration.

Staff Detection and Removal

2011 ◽  
pp. 1-39 ◽  
Author(s):  
Ichiro Fujinaga
Keyword(s):  
Image Processing ◽  
Connected Component ◽  
Optical Music Recognition ◽  
Run Length ◽  
Image Processing Techniques ◽  
Run Length Coding ◽  
Musical Scores ◽  
Music Recognition ◽  
Recognition Systems ◽  
Processing Techniques

This chapter describes the issues involved in the detection and removal of stavelines of musical scores. This removal process is an important step for many Optical Music Recognition systems and facilitates the segmentation and recognition of musical symbols. The process is complicated by the fact that most music symbols are placed on top of stavelines and these lines are often neither straight nor parallel to each other. The challenge here is to remove as much of stavelines as possible while preserving the shapes of the musical symbols, which are superimposed on stavelines. Various problematic examples are illustrated and a detailed explanation of an algorithm is presented. Image processing techniques used in the algorithm include: run-length coding, connected-component analysis, and projections.

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.

scholarly journals Real-Time Optical Music Recognition System for Dulcimer Musical Robot

2019 ◽  
Vol 23 (4) ◽  
pp. 782-790
Author(s):  
Zhe Xiao ◽  
Xin Chen ◽  
Li Zhou ◽  
◽  
◽  
...  
Keyword(s):  
Structural Analysis ◽  
Real Time ◽  
Electronic Music ◽  
Recognition Rate ◽  
Recognition System ◽  
Shape Model ◽  
Optical Music Recognition ◽  
Time Performance ◽  
Music Recognition ◽  
Music File

Traditional optical music recognition (OMR) is an important technology that automatically recognizes scanned paper music sheets. In this study, traditional OMR is combined with robotics, and a real-time OMR system for a dulcimer musical robot is proposed. This system gives the musical robot a stronger ability to perceive and understand music. The proposed OMR system can read music scores, and the recognized information is converted into a standard electronic music file for the dulcimer musical robot, thus achieving real-time performance. During the recognition steps, we treat note groups and isolated notes separately. Specially structured note groups are identified by primitive decomposition and structural analysis. The note groups are decomposed into three fundamental elements: note stem, note head, and note beams. Isolated music symbols are recognized based on shape model descriptors. We conduct tests on real pictures taken live by a camera. The tests show that the proposed method has a higher recognition rate.

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

scholarly journals A new optical music recognition system based on combined neural network

2015 ◽  
Vol 58 ◽  
pp. 1-7 ◽  
Author(s):  
Cuihong Wen ◽  
Ana Rebelo ◽  
Jing Zhang ◽  
Jaime Cardoso
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
Optical Music Recognition ◽  
Music Recognition
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