The week's pick
Reseach Article
A Novel System for Music Learning using Low Complexity Algorithms
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 6 - Number 4 |
| Year of Publication: 2013 |
| Authors: Amr Hesham, Ann Nosseir, Omar H. Karam |
10.5120/ijais13-451042
|
Amr Hesham, Ann Nosseir, Omar H. Karam . A Novel System for Music Learning using Low Complexity Algorithms. International Journal of Applied Information Systems. 6, 4 ( October 2013), 22-29. DOI=10.5120/ijais13-451042
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%.
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