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Generative AI for Music and Audio.
Generative AI for Music and Audio.
상세정보
- 자료유형
- 학위논문
- Control Number
- 0017161606
- International Standard Book Number
- 9798383196502
- Dewey Decimal Classification Number
- 004
- Main Entry-Personal Name
- Dong, Hao-Wen.
- Publication, Distribution, etc. (Imprint
- [S.l.] : University of California, San Diego., 2024
- Publication, Distribution, etc. (Imprint
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- Physical Description
- 154 p.
- General Note
- Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
- General Note
- Advisor: Berg-Kirkpatrick, Taylor;McAuley, Julian.
- Dissertation Note
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- Summary, Etc.
- 요약Generative AI has been transforming the way we interact with technology and consume content. In the next decade, AI technology will reshape how we create audio content in various media, including music, theater, films, games, podcasts, and short videos. In this dissertation, I introduce the three main directions of my research centered around generative AI for music and audio: 1) multitrack music generation, 2) assistive music creation tools, and 3) multimodal learning for audio and music. Through my research, I aim to answer the following two fundamental questions: 1) How can AI help professionals or amateurs create music and audio content? 2) Can AI learn to create music in a way similar to how humans learn music? My long-term goal is to lower the barrier of entry for music composition and democratize audio content creation.
- Subject Added Entry-Topical Term
- Computer science.
- Subject Added Entry-Topical Term
- Music.
- Index Term-Uncontrolled
- Audio synthesis
- Index Term-Uncontrolled
- Deep learning
- Index Term-Uncontrolled
- Machine learning
- Index Term-Uncontrolled
- Multimodal learning
- Index Term-Uncontrolled
- Music generation
- Added Entry-Corporate Name
- University of California, San Diego Computer Science and Engineering
- Host Item Entry
- Dissertations Abstracts International. 86-01B.
- Electronic Location and Access
- 로그인을 한후 보실 수 있는 자료입니다.
- Control Number
- joongbu:655010
MARC
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■020 ▼a9798383196502
■035 ▼a(MiAaPQ)AAI31294104
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aDong, Hao-Wen.
■24510▼aGenerative AI for Music and Audio.
■260 ▼a[S.l.]▼bUniversity of California, San Diego. ▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a154 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-01, Section: B.
■500 ▼aAdvisor: Berg-Kirkpatrick, Taylor;McAuley, Julian.
■5021 ▼aThesis (Ph.D.)--University of California, San Diego, 2024.
■520 ▼aGenerative AI has been transforming the way we interact with technology and consume content. In the next decade, AI technology will reshape how we create audio content in various media, including music, theater, films, games, podcasts, and short videos. In this dissertation, I introduce the three main directions of my research centered around generative AI for music and audio: 1) multitrack music generation, 2) assistive music creation tools, and 3) multimodal learning for audio and music. Through my research, I aim to answer the following two fundamental questions: 1) How can AI help professionals or amateurs create music and audio content? 2) Can AI learn to create music in a way similar to how humans learn music? My long-term goal is to lower the barrier of entry for music composition and democratize audio content creation.
■590 ▼aSchool code: 0033.
■650 4▼aComputer science.
■650 4▼aMusic.
■653 ▼aAudio synthesis
■653 ▼aDeep learning
■653 ▼aMachine learning
■653 ▼aMultimodal learning
■653 ▼aMusic generation
■690 ▼a0984
■690 ▼a0800
■690 ▼a0413
■71020▼aUniversity of California, San Diego▼bComputer Science and Engineering.
■7730 ▼tDissertations Abstracts International▼g86-01B.
■790 ▼a0033
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17161606▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
