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Music Information Retrieval Driven Algorithmic Music.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Music Information Retrieval Driven Algorithmic Music./
Author:	Engart, Henry Stewart, III.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	100 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
Subject:	Musical composition. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28868295
ISBN:	9798426839533

Music Information Retrieval Driven Algorithmic Music.
Engart, Henry Stewart, III.

Music Information Retrieval Driven Algorithmic Music. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 100 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--University of California, Santa Barbara, 2021.

This item must not be sold to any third party vendors.

The modern composer spends a large amount of time collecting and then sorting though digital audio files. There have been many attempts to make use of the field of Music Information Retrieval to organize and make use of the large databases of sound. This paper will present methods to both deal with large amounts of data and to harness that data for music creation itself. This document also showcases the compositional growth during the my studies at UCSB and the technical and aesthetic challenges that I had along the way. In this dissertation I will first layout the theory and history of work that has come before. I will then put my musical output in a greater musical context, explain my implementation of the theoretical concepts, presenting my own strategies, and specifically elaborating on the application of these techniques in two of my most recent compositions, Usynlig and A More Sound Output®.

ISBN: 9798426839533Subjects--Topical Terms:

3289630
Musical composition.
Subjects--Index Terms:

Algorithmic composition

Music Information Retrieval Driven Algorithmic Music.
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100 1 $a Engart, Henry Stewart, III. $3 3691320
245 1 0 $a Music Information Retrieval Driven Algorithmic Music.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 100 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Oliveira, Joao Pedro.
502 $a Thesis (Ph.D.)--University of California, Santa Barbara, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The modern composer spends a large amount of time collecting and then sorting though digital audio files. There have been many attempts to make use of the field of Music Information Retrieval to organize and make use of the large databases of sound. This paper will present methods to both deal with large amounts of data and to harness that data for music creation itself. This document also showcases the compositional growth during the my studies at UCSB and the technical and aesthetic challenges that I had along the way. In this dissertation I will first layout the theory and history of work that has come before. I will then put my musical output in a greater musical context, explain my implementation of the theoretical concepts, presenting my own strategies, and specifically elaborating on the application of these techniques in two of my most recent compositions, Usynlig and A More Sound Output®.
590 $a School code: 0035.
650 4 $a Musical composition. $3 3289630
650 4 $a Acoustics. $3 879105
650 4 $a Computer engineering. $3 621879
653 $a Algorithmic composition
653 $a Composition
653 $a Machine learning
653 $a Music
653 $a Music information retrieval
690 $a 0214
690 $a 0986
690 $a 0464
710 2 $a University of California, Santa Barbara. $b Music. $3 1025024
773 0 $t Dissertations Abstracts International $g 83-11B.
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791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28868295