Open Access   Article

Optical Music Recognition using Image Processing and Machine Learning

Prince Mathew1 , Rahul Vijayakumar2 , Aju Tom Kuriakose3 , Jesmy Sunny4 , Ramani Bai V5

Section:Research Paper, Product Type: Journal Paper
Volume-06 , Issue-10 , Page no. 18-23, Nov-2018

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v6si10.1823

Online published on Nov 30, 2018

Copyright © Prince Mathew, Rahul Vijayakumar, Aju Tom Kuriakose, Jesmy Sunny, Ramani Bai V . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: Prince Mathew, Rahul Vijayakumar, Aju Tom Kuriakose, Jesmy Sunny, Ramani Bai V, “Optical Music Recognition using Image Processing and Machine Learning”, International Journal of Computer Sciences and Engineering, Vol.06, Issue.10, pp.18-23, 2018.

MLA Style Citation: Prince Mathew, Rahul Vijayakumar, Aju Tom Kuriakose, Jesmy Sunny, Ramani Bai V "Optical Music Recognition using Image Processing and Machine Learning." International Journal of Computer Sciences and Engineering 06.10 (2018): 18-23.

APA Style Citation: Prince Mathew, Rahul Vijayakumar, Aju Tom Kuriakose, Jesmy Sunny, Ramani Bai V, (2018). Optical Music Recognition using Image Processing and Machine Learning. International Journal of Computer Sciences and Engineering, 06(10), 18-23.

           

Abstract

The ability to understand music score is a basic requirement for learning music. This paper proposes a mathematical method to find the pitch of a musical note from digital image of sheet music and a classification-based method for detecting the duration of a music note. In a sheet music, the horizontal direction can be associated with the notes starting time, whilst the vertical direction can be associated with pitch. The symbols used for a note represents its duration. Music scores sometimes need to be transposed or slightly modified, having the score in a digital format greatly reduces the time and effort required to do these. In this paper, we make use of techniques such as Run Length Encoding (RLE), Horizontal projection and Vertical Projection (X & Y projections) for Segmentation and attribute extraction. For note recognition, a classifier based system is used which returns the duration of the given input symbol. The pitch, duration and position of notes are finally given as input to a midi generation module, which generates a MIDI file corresponding to the given input music notation. There are several other applications to Optical Music Recognition (OMR) systems. Converting music scores in Braille code for the blind is yet another application of an OMR system.

Key-Words / Index Term

Optical Music Recognition, Image Processing, RLE, Classification, Machine Learning

References

[1] A. F. Desaedeleer, “Reading sheet music – openomr”,
Imperial College London,(University of London), http://sourceforge.net/projects/openomr/.
[2] R. J. Baugh Earl Gose, “Pattern Recognition and Image Analysis”, In Pattern Recognition and Image Analysis, volume 1, 2011.
[3] M. Hall, E. Frank , G. Holmes , B. Pfahringer , P. Reutemann , H. Ian : “The WEKA Data Mining Software: An Update”. SIGKDD Explorations 11, 2009.
[4] C.E. Shannon: “A Mathematical Theory of Communication”.
The Bell System Technical Journal 27, 379–423, 623–656, July, October 1948.
[5] Online. “Note names, MIDI numbers and frequencies”. http://www.phys.unsw.edu.au/jw/notes.html, June 2005.
[6] Online. “Midiutil - A Python interface for writing multi-track MIDI Files”. https://code.google.com/p/midiutil/, December 2013.
[7] Online. “PythonInMusic”. https://wiki.python.org/moin/PythonInMusic,December 2013.
[8] Online. “Note value”. http://en.wikipedia.org/wiki/Note_value, March 2014.
[9] Online. “Attribute-Relation File Format (ARFF)” https://www.cs.waikato.ac.nz/ml/weka/arff.html, November 2008.