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Raphael Sofaer
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Make chorales from the bach chorale dataset |
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1. Title of Database: Bach Chorales (time-series). | ||
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2. Sources: | ||
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(a) Chorales: Mainous and Ottman edition. | ||
Mainous, Frank D. and Robert W. Ottman, eds. 1966. | ||
The 371 Bach Chorales. Holt, Rinehart and Winston, New York. | ||
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(b) Original owners of database: | ||
Darrell Conklin | ||
ZymoGenetics Inc. | ||
1201 Eastlake Avenue East | ||
Seattle WA, 98102 | ||
conklin@zgi.com | ||
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(c) Donor of database: | ||
Same as owner. Ann Blombach of Ohio State University originally | ||
supplied me with 4-voice encodings of 100 chorales. The present | ||
database is the soprano line, converted into Lisp-readable form, | ||
and extensively corrected. | ||
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3. Past Usage: | ||
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Conklin, Darrell and Witten, Ian. 1995. Multiple Viewpoint Systems | ||
for Music Prediction. Journal of New Music Research. 24(1):51-73. | ||
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(Successfully coded chorales in a test set with an average of ~1.8 bits | ||
per pitch. Used a learning technique called Multiple Viewpoints.) | ||
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Abstract: | ||
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This paper examines the prediction and generation of music using a | ||
multiple viewpoint system, a collection of independent views of the | ||
musical surface each of which models a specific type of musical | ||
phenomena. Both the general style and a particular piece are modeled | ||
using dual short-term and long-term theories, and the model is created | ||
using machine learning techniques on a corpus of musical examples. | ||
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The models are used for analysis and prediction, and we conjecture | ||
that highly predictive theories will also generate original, | ||
acceptable, works. Although the quality of the works generated is | ||
hard to quantify objectively, the predictive power of models can be | ||
measured by the notion of entropy, or unpredictability. Highly | ||
predictive theories will produce low-entropy estimates of a musical | ||
language. | ||
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The methods developed are applied to the Bach chorale melodies. | ||
Multiple-viewpoint systems are learned from a sample of 95 chorales, | ||
estimates of entropy are produced, and a predictive theory is used to | ||
generate new, unseen pieces." | ||
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4. Dataset synopsis | ||
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Sequential (time-series) domain. Single-line melodies of 100 Bach | ||
chorales (originally 4 voices). The melody line can be studied | ||
independently of other voices. The grand challenge is to learn a | ||
generative grammar for stylistically valid chorales (see references | ||
and discussion in "Multiple Viewpoint Systems for Music Prediction"). | ||
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5. Number of Instances: 100 Chorales, each with ~45 events | ||
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6. Number of Attributes: 6 (nominal) per event | ||
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(a) start-time, measured in 16th notes from | ||
chorale beginning (time 0) | ||
(b) pitch, MIDI number (60 = C4, 61 = C#4, 72 = C5, etc.) | ||
(c) duration, measured in 16th notes | ||
(d) key signature, number of sharps or flats, | ||
positive if key signature has sharps, | ||
negative if key signature has flats | ||
(e) time signature, in 16th notes per bar | ||
(f) fermata, true or false depending on whether | ||
event is under a fermata | ||
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7. Attribute domains (all integers): | ||
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(a) {0,1,2,...} | ||
(b) {60,...,75} | ||
(c) {1,...,16} | ||
(d) {-4,...,+4} | ||
(e) {12,16} | ||
(f) {0,1} | ||
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8. Missing Attribute Values: none, repeated sections (|: :|) are | ||
not re-encoded, i.e., |:A:|B is encoded as AB. | ||
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9. Class Distribution: one class | ||
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10. The grammar describing the chorale dataset: | ||
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Dataset -> Chorale* | ||
Chorale -> (Chorale_Number (Events)) | ||
Events -> Event Events | ||
Events -> Event | ||
Event -> (Attributes) | ||
Attributes -> (st S) (pitch N) (dur D) | ||
(keysig K) (timesig T) (fermata F) | ||
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(see 7. above for attribute domains) | ||
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