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Deep Learning for Large-Scale Harmonic Analysis: A Corpus Study of Western Harmony From 1700-1900
Deep Learning for Large-Scale Harmonic Analysis: A Corpus Study of Western Harmony From 1700-1900
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202103029
- ISBN
- 9798286442218
- DDC
- 781
- 저자명
- Sailor, Malcolm.
- 서명/저자
- Deep Learning for Large-Scale Harmonic Analysis: A Corpus Study of Western Harmony From 1700-1900
- 발행사항
- [Sl] : Yale University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 380 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
- 주기사항
- Advisor: Quinn, Ian.
- 학위논문주기
- Thesis (Ph.D.)--Yale University, 2025.
- 초록/해제
- 요약This dissertation trains deep learning models for Roman numeral analysis and applies these to the analysis of a corpus of over 12,000 movements of Classical music composed between 1700 and 1900. The Roman numeral analysis models obtain performance that is, as of this writing, state of the art. At the same time, this is the first work of scholarship to employ such models at scale for a music-theoretic corpus study. Consistent with music-historical expectations, this corpus study shows that, along many dimensions, such as the distribution of keys employed or the proportion of chromatic scale degrees, harmonic complexity rose steadily over the study period, while along some other dimensions, such as the transition tendencies of Roman numeral degrees like I and V, harmonic complexity fell before rising again. We also observe that major-key practice changed more than minor-key practice, generally becoming more similar to minor-key practice, and that the usage of keys, scale degrees, and harmonies, tended to become more dispersed over the course of the nineteenth century. Finally, the corpus study reveals some striking symmetries between the modulatory behaviors of major and minor keys.
- 일반주제명
- Music theory
- 일반주제명
- Musical composition
- 키워드
- Corpus study
- 키워드
- Harmony
- 키워드
- Machine learning
- 기타저자
- Yale University Music
- 기본자료저록
- Dissertations Abstracts International. 86-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798286442218
■035 ▼a(MiAaPQ)AAI31845713
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a781
■1001 ▼aSailor, Malcolm.
■24510▼aDeep Learning for Large-Scale Harmonic Analysis: A Corpus Study of Western Harmony From 1700-1900
■260 ▼a[Sl]▼bYale University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a380 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-12, Section: B.
■500 ▼aAdvisor: Quinn, Ian.
■5021 ▼aThesis (Ph.D.)--Yale University, 2025.
■520 ▼aThis dissertation trains deep learning models for Roman numeral analysis and applies these to the analysis of a corpus of over 12,000 movements of Classical music composed between 1700 and 1900. The Roman numeral analysis models obtain performance that is, as of this writing, state of the art. At the same time, this is the first work of scholarship to employ such models at scale for a music-theoretic corpus study. Consistent with music-historical expectations, this corpus study shows that, along many dimensions, such as the distribution of keys employed or the proportion of chromatic scale degrees, harmonic complexity rose steadily over the study period, while along some other dimensions, such as the transition tendencies of Roman numeral degrees like I and V, harmonic complexity fell before rising again. We also observe that major-key practice changed more than minor-key practice, generally becoming more similar to minor-key practice, and that the usage of keys, scale degrees, and harmonies, tended to become more dispersed over the course of the nineteenth century. Finally, the corpus study reveals some striking symmetries between the modulatory behaviors of major and minor keys.
■590 ▼aSchool code: 0265.
■650 4▼aMusic theory
■650 4▼aMusical composition
■653 ▼aCorpus study
■653 ▼aHarmony
■653 ▼aMachine learning
■653 ▼aRoman numeral analysis
■690 ▼a0221
■690 ▼a0800
■690 ▼a0214
■71020▼aYale University▼bMusic.
■7730 ▼tDissertations Abstracts International▼g86-12B.
■790 ▼a0265
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17356756▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
