Statistical learning and the uncertainty of melody and bass line in music.
Authors
Publication Date
2019Journal Title
PLoS One
ISSN
1932-6203
Publisher
Public Library of Science (PLoS)
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Daikoku, T. (2019). Statistical learning and the uncertainty of melody and bass line in music.. PLoS One https://doi.org/10.1371/journal.pone.0226734
Description
Funder: Suntory Foundation; funder-id: http://dx.doi.org/10.13039/501100008887
Abstract
Statistical learning is the ability to learn based on transitional probability (TP) in sequential information, which has been considered to contribute to creativity in music. The interdisciplinary theory of statistical learning examines statistical learning as a mechanism of human learning. This study investigated how TP distribution and conditional entropy in TP of the melody and bass line in music interact with each other, using the highest and lowest pitches in Beethoven's piano sonatas and Johann Sebastian Bach's Well-Tempered Clavier. Results for the two composers were similar. First, the results detected specific statistical characteristics that are unique to each melody and bass line as well as general statistical characteristics that are shared between the melody and bass line. Additionally, a correlation of the conditional entropies sampled from the TP distribution could be detected between the melody and bass line. This suggests that the variability of entropies interacts between the melody and bass line. In summary, this study suggested that TP distributions and the entropies of the melody and bass line interact with but are partly independent of each other.
Keywords
Research Article, Physical sciences, Biology and life sciences, Social sciences, Computer and information sciences, Medicine and health sciences
Identifiers
pone-d-19-11554
External DOI: https://doi.org/10.1371/journal.pone.0226734
This record's URL: https://www.repository.cam.ac.uk/handle/1810/300184
Rights
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/
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