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Harmonic analysis using neural networks.

Tsui, Wan Shun Vincent.

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Harmonic analysis using neural networks.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Harmonic analysis using neural networks./
Author:	Tsui, Wan Shun Vincent.
Description:	96 p.
Notes:	Adviser: W. James MacLean.
Contained By:	Masters Abstracts International41-03.
Subject:	Artificial Intelligence. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ73966
ISBN:	061273966X

Harmonic analysis using neural networks.
Tsui, Wan Shun Vincent.

Harmonic analysis using neural networks. - 96 p.

Adviser: W. James MacLean.

Thesis (M.A.Sc.)--University of Toronto (Canada), 2002.

This thesis describes a system of neural networks that performs harmonic analysis on musical compositions. The system is split into four groups of Networks: Key Network, Root Network A (used to detect secondary functions), Root Network B, and Quality Network. The system is trained on 20 J. S. Bach chorales and tested on another 18 Bach chorales. Accuracies of over 90% were obtained for all four groups of Networks. Comparison with a modified version of Krumhansl's key-finding algorithm indicates Key Network outperforms it by more than 16% in overall accuracy. This work has potential for extension into music of other composers or genres. Also, this work can be used by a computer accompanist to determine the key of an improvising soloist.

ISBN: 061273966XSubjects--Topical Terms:

769149
Artificial Intelligence.

Harmonic analysis using neural networks.
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020 $a 061273966X
035 $a (UnM)AAIMQ73966
035 $a AAIMQ73966
040 $a UnM $c UnM
100 1 $a Tsui, Wan Shun Vincent. $3 1259879
245 1 0 $a Harmonic analysis using neural networks.
300 $a 96 p.
500 $a Adviser: W. James MacLean.
500 $a Source: Masters Abstracts International, Volume: 41-03, page: 0838.
502 $a Thesis (M.A.Sc.)--University of Toronto (Canada), 2002.
520 $a This thesis describes a system of neural networks that performs harmonic analysis on musical compositions. The system is split into four groups of Networks: Key Network, Root Network A (used to detect secondary functions), Root Network B, and Quality Network. The system is trained on 20 J. S. Bach chorales and tested on another 18 Bach chorales. Accuracies of over 90% were obtained for all four groups of Networks. Comparison with a modified version of Krumhansl's key-finding algorithm indicates Key Network outperforms it by more than 16% in overall accuracy. This work has potential for extension into music of other composers or genres. Also, this work can be used by a computer accompanist to determine the key of an improvising soloist.
590 $a School code: 0779.
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Engineering, Electronics and Electrical. $3 626636
650 4 $a Music. $3 516178
650 4 $a Physics, Acoustics. $3 1019086
690 $a 0413
690 $a 0544
690 $a 0800
690 $a 0986
710 2 0 $a University of Toronto (Canada). $3 1017674
773 0 $t Masters Abstracts International $g 41-03.
790 $a 0779
790 1 0 $a MacLean, W. James, $e advisor
791 $a M.A.Sc.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MQ73966