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Deep Learning for Large-Scale Harmon...

Sailor, Malcolm,

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Deep Learning for Large-Scale Harmonic Analysis: A Corpus Study of Western Harmony From 1700-1900 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning for Large-Scale Harmonic Analysis: A Corpus Study of Western Harmony From 1700-1900 // Malcolm Sailor.
Author:	Sailor, Malcolm,
Description:	1 electronic resource (380 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Music theory. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31845713
ISBN:	9798286442218

Deep Learning for Large-Scale Harmonic Analysis: A Corpus Study of Western Harmony From 1700-1900 /
Sailor, Malcolm,

Deep Learning for Large-Scale Harmonic Analysis: A Corpus Study of Western Harmony From 1700-1900 /Malcolm Sailor. - 1 electronic resource (380 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

This 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.

English

ISBN: 9798286442218Subjects--Topical Terms:

547155
Music theory.
Subjects--Index Terms:

Corpus study

Deep Learning for Large-Scale Harmonic Analysis: A Corpus Study of Western Harmony From 1700-1900 /
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520 $a This 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.
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650 4 $a Musical composition. $3 3289630
653 $a Corpus study
653 $a Harmony
653 $a Machine learning
653 $a Roman numeral analysis
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