The Optical Neume Recognition Project

2012

For centuries, music has been shared and remembered by two traditions: aural transmission and in the form of written documents normally called musical scores. Many of these scores exist in the form of unpublished manuscripts and hence they are in danger of being lost through the normal ravages of time. To preserve the music some form of typesetting or, ideally, a computer system that can automatically decode the symbolic images and create new scores is required. Programs analogous to optical character recognition systems called optical music recognition (OMR) systems have been under intensive development for many years. However, the results to date are far from ideal. Each of the proposed methods emphasizes different properties and therefore makes it difficult to effectively evaluate its competitive advantages. This article provides an overview of the literature concerning the automatic analysis of images of printed and handwritten musical scores. For self-containment and for the benefit of the reader, an introduction to OMR processing systems precedes the literature overview. The following study presents a reference scheme for any researcher wanting to compare new OMR algorithms against well-known ones.

On-Line and Off Line Recognition

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.

Proceedings First International Conference on WEB Delivering of Music. WEDELMUSIC 2001, 2001

Interactive Multimedia Music Technologies

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 O 3 MR (object oriented optical music recognition) system is presented. It allows producing from the image of a music sheet the symbolic representation and saving 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 fields of image understanding. The development of the O 3 MR 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.

DATESO, 2015

Music has been always an integral part of human culture. In our computer age, it is not surprising that there is a growing interest to store music in a digitized form. Optical music recognition (OMR) refers to a discipline that investigates music score recognition systems. This is similar to well-known optical character recognition systems, except OMR systems try to automatically transform scanned sheet music into a computer-readable format. In such a digital format, semantic information is also stored (instrumentation, notes, pitches and duration, contextual information, etc.). This article introduces the OMR field and presents an overview of the relevant literature and basic techniques. Practical challenges and questions arising from the automatic recognition of music notation and its semantic interpretation are discussed as well as the most important open issues.

2018

Significant digitization efforts have resulted in large music collections, which comprise music-related documents of various types and formats including text, symbolic data, audio, image, and video. For example, in the case of an opera, there typically exist digitized versions of the libretto, different editions of the musical score, as well as a large number of performances available as audio and video recordings. In the field of music information retrieval (MIR), great efforts are directed towards the development of technologies that allow users to access and explore music in all its different facets. For example, during playback of a CD recording, a digital music player may present the corresponding musical score while highlighting the current playback position within the score. On demand, additional information about the performance, the instrumentation, the melody, or other musical attributes may be automatically presented to the listener. A suitable user interface displays the musical score or the structure of the current piece of music, which allows the user to directly jump to any part within the recording without tedious fast-forwarding and rewinding. The project Freischütz Digital (FreiDi) offered an interdisciplinary platform for musicologists and computer scientists to jointly develop and introduce computer-based methods that enhance human involvement with music. The opera Der Freischütz by Carl Maria von Weber served as an example scenario. This work plays a central role in the Western music literature and is of high relevance for musicological studies. Also, this opera was chosen because of its rich body of available sources-including different versions of the musical score, the libretto, and audio recordings. One goal of the project was to explore techniques for establishing a virtual archive of relevant digitized objects, including symbolic representations of the autograph score and other musical sources (encoded in MEI), 1 transcriptions and facsimiles of libretti and other 1 MEI stands for the Music Encoding Initiative, which is an open-source effort to define a system for encoding musical documents in a machine-readable structure.

1994

IAEME PUBLICATION, 2020

Optical music symbol recognition facilitates to transcribe the music sheet into machine-readable format so that it can be used for various applications by converting it into midi format. Most of the works in the past have focused on the recognition of printed music symbols and a few on online music symbols. Earlier methods work very well for printed music symbol recognition. However, their performance is limited to clean and binarized documents. Handwritten music symbol recognition is explored a little as it has several challenges such as variation in writing styles, document degradation, noise etc. In this paper, we have investigated the performance of well-known texture descriptor namely Histogram of Oriented Gradients (HOG) for the Old Handwritten Music Symbol Recognition on the publicly available dataset. Support Vector Machine and K-Nearest Neighbor Classifiers were employed for the music symbol classification with K –Fold Cross Validation Technique. We have achieved encouraging results and shown the comparative analysis of various sizes of cell of computing HOG.

2015

Optical music recognition (OMR) is the task of recognizing images of musical scores. In this paper, improved algorithms for the first steps of optical music recognition were developed, which facilitated bulk annotation of scanned scores for use in an interactive score display system. Creating an initial annotation by OMR and verifying by hand substantially reduced the manual effort required to process scanned scores to be used in a live performance setting.

Arti musices, 2017

generaliter sumpta, obiective quasi ad omnia se extendit, ad Deum et ad creaturas, incorporeas et corporeas, coelestes et humanas, ad scientias theoricas et practicas«, Roger Bragard (ed.