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Generative AI for Music and Audio
Generative AI for Music and Audio
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211151420
- ISBN
- 9798383196502
- DDC
- 004
- 저자명
- Dong, Hao-Wen.
- 서명/저자
- Generative AI for Music and Audio
- 발행사항
- [Sl] : University of California, San Diego, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 154 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
- 주기사항
- Advisor: Berg-Kirkpatrick, Taylor;McAuley, Julian.
- 학위논문주기
- Thesis (Ph.D.)--University of California, San Diego, 2024.
- 초록/해제
- 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.
- 일반주제명
- Computer science
- 일반주제명
- Music
- 키워드
- Audio synthesis
- 키워드
- Deep learning
- 키워드
- Machine learning
- 키워드
- Music generation
- 기타저자
- University of California, San Diego Computer Science and Engineering
- 기본자료저록
- Dissertations Abstracts International. 86-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
■007cr#unu||||||||
■020 ▼a9798383196502
■035 ▼a(MiAaPQ)AAI31294104
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aDong, Hao-Wen.
■24510▼aGenerative AI for Music and Audio
■260 ▼a[Sl]▼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.
