東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Towards automatic musical instrument...

Park, Tae Hong.

FindBook

Google Book

Amazon

博客來

Towards automatic musical instrument timbre recognition.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards automatic musical instrument timbre recognition./
作者:	Park, Tae Hong.
面頁冊數:	244 p.
附註:	Source: Dissertation Abstracts International, Volume: 65-08, Section: A, page: 2830.
Contained By:	Dissertation Abstracts International65-08A.
標題:	Music. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3143551
ISBN:	0496013270

Towards automatic musical instrument timbre recognition.
Park, Tae Hong.

Towards automatic musical instrument timbre recognition. - 244 p.

Source: Dissertation Abstracts International, Volume: 65-08, Section: A, page: 2830.

Thesis (Ph.D.)--Princeton University, 2004.

This dissertation is comprised of two parts---focus on issues concerning research and development of an artificial system for automatic musical instrument timbre recognition and musical compositions. The technical part of the essay includes a detailed record of developed and implemented algorithms for feature extraction and pattern recognition. A review of existing literature introducing historical aspects surrounding timbre research, problems associated with a number of timbre definitions, and highlights of selected research activities that have had significant impact in this field are also included. The developed timbre recognition system follows a bottom-up, data-driven model that includes a pre-processing module, feature extraction module, and a RBF/EBF (Radial/Elliptical Basis Function) neural network-based pattern recognition module. 829 monophonic samples from 12 instruments have been chosen from the Peter Siedlaczek library (Best Service) and other samples from the Internet and personal collections. Significant emphasis has been put on feature extraction development and testing to achieve robust and consistent feature vectors that are eventually passed to the neural network module. In order to avoid a garbage-in-garbage-out (GIGO) trap and improve generality, extra care was taken in designing and testing the developed algorithms using various dynamics, different playing techniques, and a variety of pitches for each instrument with inclusion of attack and steady-state portions of a signal. Most of the research and development was conducted in Matlab. The compositional part of the essay includes brief introductions to "A d'Ess Are ," "Aboji," "48 13 N, 16 20 O," and "pH-SQ." A general outline pertaining to the ideas and concepts behind the architectural designs of the pieces including formal structures, time structures, orchestration methods, and pitch structures are also presented.

ISBN: 0496013270Subjects--Topical Terms:

516178
Music.

Towards automatic musical instrument timbre recognition.
LDR:02871nmm 2200313 4500 001 1844588
005 20051017073525.5
008 130614s2004 eng d
020 $a 0496013270
035 $a (UnM)AAI3143551
035 $a AAI3143551
040 $a UnM $c UnM
100 1 $a Park, Tae Hong. $3 1932777
245 1 0 $a Towards automatic musical instrument timbre recognition.
300 $a 244 p.
500 $a Source: Dissertation Abstracts International, Volume: 65-08, Section: A, page: 2830.
500 $a Advisers: Paul Lansky; Perry Cook.
502 $a Thesis (Ph.D.)--Princeton University, 2004.
520 $a This dissertation is comprised of two parts---focus on issues concerning research and development of an artificial system for automatic musical instrument timbre recognition and musical compositions. The technical part of the essay includes a detailed record of developed and implemented algorithms for feature extraction and pattern recognition. A review of existing literature introducing historical aspects surrounding timbre research, problems associated with a number of timbre definitions, and highlights of selected research activities that have had significant impact in this field are also included. The developed timbre recognition system follows a bottom-up, data-driven model that includes a pre-processing module, feature extraction module, and a RBF/EBF (Radial/Elliptical Basis Function) neural network-based pattern recognition module. 829 monophonic samples from 12 instruments have been chosen from the Peter Siedlaczek library (Best Service) and other samples from the Internet and personal collections. Significant emphasis has been put on feature extraction development and testing to achieve robust and consistent feature vectors that are eventually passed to the neural network module. In order to avoid a garbage-in-garbage-out (GIGO) trap and improve generality, extra care was taken in designing and testing the developed algorithms using various dynamics, different playing techniques, and a variety of pitches for each instrument with inclusion of attack and steady-state portions of a signal. Most of the research and development was conducted in Matlab. The compositional part of the essay includes brief introductions to "A d'Ess Are ," "Aboji," "48 13 N, 16 20 O," and "pH-SQ." A general outline pertaining to the ideas and concepts behind the architectural designs of the pieces including formal structures, time structures, orchestration methods, and pitch structures are also presented.
590 $a School code: 0181.
650 4 $a Music. $3 516178
650 4 $a Computer Science. $3 626642
650 4 $a Physics, Acoustics. $3 1019086
650 4 $a Artificial Intelligence. $3 769149
690 $a 0413
690 $a 0984
690 $a 0986
690 $a 0800
710 2 0 $a Princeton University. $3 645579
773 0 $t Dissertation Abstracts International $g 65-08A.
790 1 0 $a Lansky, Paul, $e advisor
790 1 0 $a Cook, Perry, $e advisor
790 $a 0181
791 $a Ph.D.
792 $a 2004
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3143551