3 research outputs found

    When in Rome: A Meta-corpus of Functional Harmony

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    โ€˜When in Romeโ€™ brings together all human-made, computer-encoded, functional harmonic analyses of music. This amounts in total to over 2,000 analyses of 1,500 distinct works. The most obvious motivation is scale: gathering these datasets together leads to a corpus large and varied enough for tasks including machine learning for automatic analysis, composition, and classification, as well as at-scale anthology creation and more. Further benefits include bringing together a range of different composers and genres (previous datasets typically limit themselves to one context), and of analytical perspectives on those works. We offer this data in as ready-to-use and reproducible a state as possible at http://github.com/MarkGotham/When-in-Rome, with code and documentation for all tasks reported here, including corpus conversion routines and feature extraction

    Possibility of Organicity in Atonal Harmonic System : A Study on the Classification of Harmonic Cadence in Ligetiโ€™s Piano ร‰tudes

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    ๋น„๊ณตํ†ต๊ด€์Šต์ ์ธ ํ˜„๋Œ€์Œ์•…์€ ์ž‘ํ’ˆ์˜ ์Œ์กฐ์ง ๋ฐ ํ™”์„ฑ์ฒด๊ณ„์˜ ๊ฐœ๋ณ„์„ฑ์œผ๋กœ ์ธํ•ด, ํ™”์„ฑ์  ๋งฅ๋ฝ์˜ ํŒŒ์•…์ด ์–ด๋ ค์›Œ์กŒ๋‹ค. ์ด๋Ÿฌํ•œ ๋‚œ์ ์— ๋”ฐ๋ผ, ์—ฌ๋Ÿฌ ์ƒˆ๋กœ์šด ๋ถ„์„๋ฐฉ๋ฒ•์ด ์‹œ๋„๋˜์—ˆ์œผ๋‚˜, ์ด์ „์˜ ๊ณตํ†ต๊ด€์Šต์ฒด๊ณ„์˜ ์ž‘ํ’ˆ์— ๋Œ€ํ•œ ๋ถ„์„๋ชจ๋ธ๊ณผ ๊ฐ™์€ ์•…๊ณก ์ „์ฒด์— ๋Œ€ํ•œ ํ™”์„ฑ์  ์œ ๊ธฐ์„ฑ์˜ ๊ณ ๋ ค์—๋Š” ์•„์‰ฌ์šด ์ ์ด ์žˆ๋‹ค. ๋ณธ๊ณ ์—์„œ๋Š” ์ฃ„๋ฅด์ง€ ๋ฆฌ๊ฒŒํ‹ฐ(Gyรถrgy Ligeti, 1923~2006)์˜ โŸชํ”ผ์•„๋…ธ ์—ํŠ€๋“œโŸซ (ร‰tudes pour piano)๋ฅผ ๋Œ€์ƒ์œผ๋กœ, ํ™”์„ฑ์  ์ฃผ์š”์ง€์  ์ค‘ ํ•˜๋‚˜์ธ ์ข…์ง€์— ๋‚˜ํƒ€๋‚˜๋Š” ํ™”์„ฑ์  ์ธก๋ฉด์„ ๋™์ ๊ตฌ๋ฌธ๋ก ์  ๊ด€์ ์— ๊ธฐ์ดˆํ•˜์—ฌ ์œ ํ˜•ํ™”๋ฅผ ์‹œ๋„ํ•จ์œผ๋กœ์จ, ๋น„๊ณตํ†ต๊ด€์Šต์Œ์•…์—์„œ์˜ ํ™”์„ฑ์  ์ธก๋ฉด์— ์ ‘๊ทผํ•  ์ˆ˜ ์žˆ๋Š” ํ•˜๋‚˜์˜ ์ง€์ ์„ ๋ณด๋‹ค ๊ตฌ์ฒดํ™”ํ•˜๋Š” ๊ฒƒ์„ ์‹œ๋„ํ•œ๋‹ค. ํ™”์„ฑ์  ์–‘์ƒ์— ๋Œ€ํ•œ ์ ‘๊ทผ์„ ์œ„ํ•˜์—ฌ, ๊ธฐ์ค€์ด ๋  ํ™”์„ฑ์  ๊ธฐ๋ณธ์›๋ฆฌ๋ฅผ ๋จผ์ € ์กฐ๋ช…ํ•˜๊ณ , ๋ฆฌ๊ฒŒํ‹ฐ ์—ํŠ€๋“œ ์ „๊ณก์— ๋‚˜ํƒ€๋‚˜๋Š” ๊ฐ ๋ฒ”์ฃผ์˜ ํ™”์„ฑ์  ์†Œ์žฌ์— ๋Œ€ํ•ด ํ™”์„ฑ์  ๊ธฐ๋ณธ์›๋ฆฌ์— ๊ฒฌ์ฃผ์–ด ๊ณ ์ฐฐํ•˜์˜€๋‹ค. ์ข…์ง€์—์„œ์˜ ํ™”์„ฑ์  ์–‘์ƒ์„, ๊ณก์—์„œ ํ˜•์„ฑ๋˜๋Š” ์Œ์กฐ์ง ๋ฐ ํ™”์„ฑ์ฒด๊ณ„์˜ ๊ตฌ์กฐ์— ๊ฒฌ์ฃผ์–ด ๋™์ ๊ตฌ๋ฌธ๋ก ์˜ ๊ด€์ ์—์„œ ์–ด๋– ํ•œ ์˜๋ฏธ๋กœ ์ดํ•ด๋  ์ˆ˜ ์žˆ๋Š”๊ฐ€์— ๋”ฐ๋ผ, ๊ธฐ๋ณธ์  ์œ ํ˜•๊ณผ ๋ณตํ•ฉ์  ์œ ํ˜•์œผ๋กœ ๋ถ„๋ฅ˜ํ•˜๊ณ , ๊ฐ ๋ถ„๋ฅ˜ ๋‚ด์—์„œ ์„ธ๋ถ€์ ์ธ ํ™”์„ฑ์–‘์ƒ์˜ ์ฐจ์ด๋ฅผ ๋ฐ˜์˜ํ•œ ํ•˜์œ„๋ถ„๋ฅ˜๋ฅผ ํฌํ•จํ•˜์—ฌ, ๊ทธ์— ๋”ฐ๋ฅธ ์Œ์•…์  ์˜๋ฏธ์˜ ์„ธ๋ถ€์  ์ฐจ์ด๋ฅผ ๋ฐ˜์˜ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•˜์—ฌ ๋น„๊ณตํ†ต๊ด€์Šต์Œ์•…์— ์žˆ์–ด์„œ๋„ ์Œ ๋ฐ ํ™”์„ฑ์†Œ์žฌ์˜ ์ƒํ˜ธ๊ด€๊ณ„ ๋ฐ ์กฐ์ง์— ๋”ฐ๋ผ, ํ™”์„ฑ์ด ์ข…์ง€๋ผ๋Š” ์ค‘์š”ํ•œ ์Œ์•…์  ๊ตญ๋ฉด์—์„œ ์„ธ๋ฐ€ํ•˜๊ฒŒ ๋ถ„ํ™”๋œ ์Œ์•…์  ์˜๋ฏธ๋ฅผ ํ˜•์„ฑํ•จ์„ ๊ตฌ์ฒด์ ์œผ๋กœ ํŒŒ์•…ํ•˜์˜€๋‹ค.In the non-traditional, contemporary music, it has become difficult to determine the harmonic context due to its individual harmonic system. Although, this difficulty generates several new analysis approaches, they are still inefficient to explain the harmonic organicity in the whole piece compared to the previous common method in analysis to tonal music. The aim of this study is approaching the harmonic aspect in cadences by researching โŸชร‰tudes pour pianoโŸซ by Gyรถrgy Ligeti (1923~2006), to derive one point of the harmonic aspect in non-conventional music and explore it in detailed way, by classifying the several categories of the harmonic aspects in cadence based on the perspective of Dynamic syntax. In order to approach the harmonic aspect, the basic principle to be used as a standard in this research is explored first and considered in the basic principle of harmony of each category in Ligetiโ€™s etudes. The harmonic aspects in cadences are categorized as fundamental type and multiplicative type by the structure of tone and harmonic system in the piece and how they are possible to be interpreted in the respect of Dynamic syntax. In addition, subclassifications are derived from respective categories by subtle differences in harmonic aspects. Therefore, it also reflects the detailed difference of musical meanings. This research suggests the harmony creates detailed musical aspects in the cadence which is the significant element in the perspective of music by the organic relationship by tone organization and harmonic system in non-traditional music.I. ์„œ๋ก  1 II. ๋ณธ๋ก  6 1. ์›๋ฆฌ 6 1) ์ƒ๊ด€ํ™”relativization ์›๋ฆฌ 10 2) ์กฐ์งํ™”organization ๋ฐ ์œ„๊ณ„ํ™”hierarchization์˜ ์›๋ฆฌ 13 3) ๊ฒฐํ•ฉcombining๊ณผ ๊ณต๊ฐ„ํ™”spatialization์˜ ์›๋ฆฌ 17 4) ๋ฐฉํ–ฅ์„ฑdirectivity๊ณผ ์šด๋™motion ๋ฐ ์ง„ํ–‰progression์˜ ์›๋ฆฌ 21 5) ์•ˆ์ •ํ™”stabilization ๋ฐ ์•ˆ์ •ยท๋ถˆ์•ˆ์ •์„ฑstability/instability์˜ ์›๋ฆฌ 24 2 . ์–‘์ƒ 26 1) ์†Œ์žฌ 26 (1) ์Œ๊ณ  27 (2) ์ง„ํ–‰์Œ์ • 40 (3) ํ™”์„ฑ์  ์Œ์ • ๋ฐ ํ™”์Œ 56 (4) ์Œ๊ณ„ 141 2) ์กฐ์ง 167 3. ์œ ํ˜• 181 1) ํšŒ๊ท€์ข…์ง€ 182 (1) ์™„์ „ ํšŒ๊ท€์ข…์ง€ 183 (2) ๋ถˆ์™„์ „ ํšŒ๊ท€์ข…์ง€ 186 (3) ์ค‘๊ฐ„ ํšŒ๊ท€์ข…์ง€ 189 (4) ๋Œ๋ฐœ ํšŒ๊ท€์ข…์ง€ 193 2) ์ดํƒˆ์ข…์ง€ 196 (1) ์ผํƒˆ์  ์ดํƒˆ์ข…์ง€ 196 (2) ์ง„ํ–‰์  ์ดํƒˆ์ข…์ง€ 202 (3) ๋Œ€๋ฆฝ์  ์ดํƒˆ์ข…์ง€ 205 (4) ํšŒํ”ผ์  ์ดํƒˆ์ข…์ง€ 206 3) ์ค‘๊ฐ„์ข…์ง€ 209 (1) ์ง€์—ฐ์  ์ค‘๊ฐ„์ข…์ง€ 210 (2) ์ฃผ๋ณ€์  ์ค‘๊ฐ„์ข…์ง€ 214 (3) ์ค‘๋‹จ์  ์ค‘๊ฐ„์ข…์ง€ 217 4) ๋ณ€ํ™”์ข…์ง€ 227 (1) ๋ณ€ํ˜•์  ๋ณ€ํ™”์ข…์ง€ 227 (2) ์ „ํ™˜์  ๋ณ€ํ™”์ข…์ง€ 231 (3) ์ด๋„์  ๋ณ€ํ™”์ข…์ง€ 234 5) ํ˜ผํ•ฉ์ข…์ง€ 239 (1) ์ƒ์ถฉ์  ํ˜ผํ•ฉ์ข…์ง€ 239 (2) ์œ ์‚ฌ ํ˜ผํ•ฉ์ข…์ง€ 241 (3) ๋‹ค์›์  ํ˜ผํ•ฉ์ข…์ง€ 244 (4) ๊ฒฐํ•ฉ์„ฑ ํ˜ผํ•ฉ์ข…์ง€ 245 III. ๊ฒฐ๋ก  250 ์ฐธ๊ณ ๋ฌธํ—Œ 251 ์ˆ˜๋ก์•…๋ณด๋ชฉ๋ก 260 ํ‘œ๋ชฉ๋ก 267 Abstract 269์„

    Relevance of musical features for cadence detection

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    International audienceCadences, as breaths in music, are felt by the listener or studied by the theorist by combining harmony, melody, texture and possibly other musical aspects. We formalize and discuss the significance of 44 cadential features, correlated with the occurrence of cadences in scores. These features describe properties at the arrival beat of a cadence and its surroundings, but also at other onsets heuristically identified to pinpoint chords preparing the cadence. The representation of each beat of the score as a vector of cadential features makes it possible to reformulate cadence detection as a classification task. An SVM classifier was run on two corpora from Bach and Haydn totaling 162 perfect authentic cadences and 70 half cadences. In these corpora, the classifier correctly identified more than 75% of perfect authentic cadences and 50% of half cadences, with low false positive rates. The experiment results are consistent with common knowledge that classification is more complex for half cadences than for authentic cadences
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