A survey of computer uses in music

This thesis covers research into the mathematical basis inherent in music including review of projects related to optical character recognition (OCR) of musical symbols. Research was done about fractals creating new pieces by assigning pitches to numbers. Existing musical pieces can be taken apart and reassembled creating new ideas for composers. Musical notation understanding is covered and its requirement for the recognition of a music sheet by the computer for editing and reproduction purposes is explained. The first phase of a musical OCR was created in this thesis with the recognition of staff lines on a good quality image. Modifications will need to be made to take care of noise and tilted images that may result from scanning

Proceedings of the 4th International Workshop on Reading Music Systems

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 4th International Workshop on Reading Music Systems, held online on Nov. 18th 2022.Comment: Proceedings edited by Jorge Calvo-Zaragoza, Alexander Pacha and Elona Shatr

A new parallel bat algorithm for musical note recognition

Music is a universal language that does not require an interpreter, where feelings and sensitivities are united, regardless of the different peoples and languages, The proposed system consists of two main stages: the process of extracting important properties using the linear discrimination analysis (LDA) This step is carried out after the initial treatment process using various procedures to remove musical lines, The second stage describes the recognition process using the bat algorithm, which is one of the metaheuristic algorithms after modifying the bat algorithm to obtain better discriminating results. The proposed system was supported by parallel implementation using the (Developed Bat Algorithm DBA), which increased the speed of implementation significantly. The method was applied to 1250 different images of musical notes. The proposed system was implemented using MATLAB R2016a, Work was done on a Windows10 Processor OS (Intel ® Core TM i5-7200U CPU @ 2.50GHZ 2.70GHZ) computer

Deep watershed detector for music object recognition

Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline. In this paper, we introduce a novel object detection method, based on synthetic energy maps and the watershed transform, called Deep Watershed Detector (DWD). Our method is specifically tailored to deal with high resolution images that contain a large number of very small objects and is therefore able to process full pages of written music. We present state-of-the-art detection results of common music symbols and show DWD’s ability to work with synthetic scores equally well as on handwritten music

Deep watershed detector for music object recognition

Optical Music Recognition (OMR) is an important and challenging area within music information retrieval, the accurate detection of music symbols in digital images is a core functionality of any OMR pipeline. In this paper, we introduce a novel object detection method, based on synthetic energy maps and the watershed transform, called Deep Watershed Detector (DWD). Our method is specifically tailored to deal with high resolution images that contain a large number of very small objects and is therefore able to process full pages of written music. We present state-of-the-art detection results of common music symbols and show DWD’s ability to work with synthetic scores equally well as on handwritten music

Program: Graduate Research Achievement Day 2017

Full program for 2017 Graduate Research Achievement Day.https://digitalcommons.odu.edu/graduateschool_achievementday2017-18_programs/1001/thumbnail.jp