Name
Abstract | ||
|---|---|---|
Cadences, 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. |
Year | Venue | Field |
|---|---|---|
2018 | ISMIR | Cadence,Music information retrieval,Musical,Computer science,Voice leading,Speech recognition,Beat (music),Svm classifier,Chord (music),Classifier (linguistics) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Louis Bigo | 1 | 2 | 3.15 |
| Laurent Feisthauer | 2 | 0 | 1.35 |
| Mathieu Giraud | 3 | 124 | 15.28 |
| Florence Levé | 4 | 51 | 10.20 |