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AI-Generated Music: Scoring Modern M...

Li, Elliona Ziyan Song,

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AI-Generated Music: Scoring Modern Media /

Record Type:	Electronic resources : Monograph/item
Title/Author:	AI-Generated Music: Scoring Modern Media // Elliona Ziyan Song Li.
Author:	Li, Elliona Ziyan Song,
Description:	1 electronic resource (103 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
Subject:	Musical composition. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31764236
ISBN:	9798346849865

AI-Generated Music: Scoring Modern Media /
Li, Elliona Ziyan Song,

AI-Generated Music: Scoring Modern Media /Elliona Ziyan Song Li. - 1 electronic resource (103 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

This dissertation examines the role of artificial intelligence in music composition, focusing on AI-generated music for modern media. It traces the evolution of AI in music, from early computational experiments to contemporary models like Google's Magenta, OpenAI's Jukebox, and Meta's MusicGen. Central to the research is the fine-tuning and evaluation of MusicGen to align with specific stylistic and creative goals.The study explores the potential of AI to augment artistic processes while addressing ethical concerns such as copyright and authorship. By combining technical analysis with philosophical inquiry, this work positions AI as a tool to enhance creativity, contributing to the ongoing discourse on the intersection of technology and the arts.

English

ISBN: 9798346849865Subjects--Topical Terms:

3289630
Musical composition.
Subjects--Index Terms:

AI music

AI-Generated Music: Scoring Modern Media /
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020 $a 9798346849865
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100 1 $a Li, Elliona Ziyan Song, $e author. $3 3821429
245 1 0 $a AI-Generated Music: Scoring Modern Media / $c Elliona Ziyan Song Li.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (103 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
500 $a Includes supplementary digital materials.
500 $a Advisors: Krouse, Ian Committee members: Burke, Jeffrey Aaron; Anderson, Steven Franklin; Beard, Jonathan Ryan; Rhie, Kay Kyurim; Golub, Peter Richard.
502 $b Ph.D. $c University of California, Los Angeles $d 2024.
520 $a This dissertation examines the role of artificial intelligence in music composition, focusing on AI-generated music for modern media. It traces the evolution of AI in music, from early computational experiments to contemporary models like Google's Magenta, OpenAI's Jukebox, and Meta's MusicGen. Central to the research is the fine-tuning and evaluation of MusicGen to align with specific stylistic and creative goals.The study explores the potential of AI to augment artistic processes while addressing ethical concerns such as copyright and authorship. By combining technical analysis with philosophical inquiry, this work positions AI as a tool to enhance creativity, contributing to the ongoing discourse on the intersection of technology and the arts.
546 $a English
590 $a School code: 0031
650 4 $a Musical composition. $3 3289630
650 4 $a Music. $3 516178
650 4 $a Web studies. $3 2122754
653 $a AI music
653 $a AI-assisted composition
653 $a Film scoring
653 $a Generative AI
653 $a Immersive media
653 $a Virtual Reality
690 $a 0214
690 $a 0800
690 $a 0646
690 $a 0413
710 2 $a University of California, Los Angeles. $b Music 0579. $e degree granting institution. $3 3819696
720 1 $a Krouse, Ian $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-06B.
790 $a 0031
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31764236