Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

May Edition 2020
International Journal of Applied Information Systems solicits high quality original research papers for the May 2020 Edition of the journal. The last date of research paper submission is April 15, 2020.

A Novel System for Music Learning using Low Complexity Algorithms

Amr Hesham, Ann Nosseir, Omar H. Karam Published in Signal Processing

A Novel System for Music Learning using Low Complexity Algorithms
International Journal of Applied Information Systems
Year of Publication: 2013
© 2012 by IJAIS Journal
Series Volume 6 Number 4
Authors Amr Hesham, Ann Nosseir, Omar H. Karam
10.5120/ijais13-451042
Download full text

  1. Amr Hesham, Ann Nosseir and Omar H Karam. Article: A Novel System for Music Learning using Low Complexity Algorithms. International Journal of Applied Information Systems 6(4):22-29, October 2013. BibTeX

    @article{key:article,
	author = "Amr Hesham and Ann Nosseir and Omar H. Karam",
	title = "Article: A Novel System for Music Learning using Low Complexity Algorithms",
	journal = "International Journal of Applied Information Systems",
	year = 2013,
	volume = 6,
	number = 4,
	pages = "22-29",
	month = "October",
	note = "Published by Foundation of Computer Science, New York, USA"
}

Abstract

This paper introduces a music learning system that uses new low complexity algorithms and aims to solve the four most common problems faced by self-learning beginner pianists: reading music sheets, playing fast tempo music pieces, verifying the key of a music piece, and finally evaluating their own performances. In order to achieve these aims, the system proposes a monophonic automatic music transcription system capable of detecting notes in the range from G2 to G6. It uses an autocorrelation algorithm along with a binary search based algorithm in order to map the detected frequencies of the individual notes of a musical piece to the nearest musical frequencies. To enable playing fast music, the system uses a MIDI player equipped with a virtual piano as well as section looping and speed manipulation functionalities to enable the user to start learning a musical piece slowly and build up speed. Furthermore, it applies the Krumhansl-Schmuckler key-finding algorithm along with the correlation algorithm to identify the key of a musical piece. A musical performance evaluation algorithm is also introduced which compares the original performance with that of the learner's producing a quantitative similarity measure between the two. The experimental evaluation shows that the system is capable of detecting notes in the range from G2 to G6 with an accuracy of 88. 7% in addition to identifying the key of a musical piece with an accuracy of 97. 1%.

Reference

  1. Klapuri , A. & Virtanen , T. , (2009), " Automatic music transcription," In Havelock, D. , Kuwano, S. , & Vorländer, M. Handbook of signal processing in acoustics, Springer New York, NY, Vol. IV, pp. 277-303.
  2. Good, M. (2001), "Musicxml: An internet-friendly format for sheet music", XML Conference and Expo, Montvale, NJ, February 2001, pp. 3-4.
  3. Scogin, N. (2010), Barron's AP music theory, Barron's Educational Series. N. Y.
  4. Brandao, M. , Wiggins, G. , & Pain, H. (1999) "Computers in music education," In Geraint Wiggins, AISB'99 Symposium on Musical Creativity, pp. 82-88.
  5. Burns, H. L. , & Capps, C. G. (1988), Foundations of intelligent tutoring systems: An introduction, In Polson, M. C. & Richardson, J. J. , Foundations of intelligent tutoring systems, Hillsdale: Lawrence Erlbaum Associates, pp. 1-19.
  6. Scheirer, E. D. (1995), "Extracting expressive performance information from recorded music", Master's thesis, Massachusetts Institute of Technology, Media Laboratory
  7. Moore, F. R. (1990), Elements of computer music, Prentice-Hall, Inc. , NY
  8. Bello, J. P. , Monti, G. , & Sandler, M. (2000), "Techniques for automatic music transcription", In the First International Symposium on Music Information Retrieval ISMIR-00, Plymouth, Massachusetts, USA. October 2000, pp. 23-25.
  9. Costantini, G. , Todisco, M. , Perfetti, R. , Basili, R. , & Casali, D. (2010), "Memory Based Automatic Music Transcription System for Percussive Pitched Instruments", In 1st International Multi-Conference on Complexity, Informatics and Cybernetics (IMCIC), Orlando, Florida, USA, April 2010
  10. Benetos, E. , & Dixon, S. (2011), "Multiple-F0 Estimation and Note Tracking for using a convolutive probabilistic model". Music Information Retrieval Evaluation eXchange, Miami, Florida, USA, October 2011.
  11. Benetos, E. , Klapuri, A. , & Dixon, S. (2012), "Score-informed transcription for automatic piano tutoring", IEEE Signal Processing Conference (EUSIPCO), Bucharest, August 2012, pp. 2153-2157
  12. Guo, Y. , & Tang, J. (2012), "A Combined Mathematical Treatment for a Special Automatic Music Transcription System", Abstract and Applied Analysis, December, Vol. 2012, pp. 13
  13. Hasegawa, B. H. (1987), "The physics of medical x-ray imaging. Medical Physics Pub Corp; June, ed. 2.
  14. Middleton, G. (2003). Pitch Detection Algorithms. Retrieved from the Connexions Web site: http://cnx. org/content/m11714/1. 2/
  15. Temperley, D. (1999). "What's key for key? The Krumhansl-Schmuckler key-finding algorithm reconsidered". Music Perception, Vol. 17, pp. 65-100.

Keywords

Music learning, automatic music transcription, key finding, monophonic music