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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
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  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

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Keywords

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