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dc.contributor.authorDaikoku, Tatsuya
dc.date.accessioned2019-12-20T01:15:02Z
dc.date.available2019-12-20T01:15:02Z
dc.date.issued2019-12-19
dc.date.submitted2019-04-23
dc.identifier.otherpone-d-19-11554
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/300184
dc.descriptionFunder: Suntory Foundation; funder-id: http://dx.doi.org/10.13039/501100008887
dc.description.abstractStatistical 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.
dc.languageen
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectResearch Article
dc.subjectPhysical sciences
dc.subjectBiology and life sciences
dc.subjectSocial sciences
dc.subjectComputer and information sciences
dc.subjectMedicine and health sciences
dc.titleStatistical learning and the uncertainty of melody and bass line in music
dc.typeArticle
dc.date.updated2019-12-20T01:15:01Z
dc.identifier.doi10.17863/CAM.47255
dcterms.dateAccepted2019-12-03
rioxxterms.versionofrecord10.1371/journal.pone.0226734
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Rhodes, Darren
dc.contributor.orcidDaikoku, Tatsuya [0000-0002-0224-6704]
dc.identifier.eissn1932-6203


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)