dc.contributor.author | Hartmann, Martin | |
dc.contributor.author | Lartillot, Olivier | |
dc.contributor.author | Toiviainen, Petri | |
dc.date.accessioned | 2016-12-20T07:26:35Z | |
dc.date.available | 2016-12-20T07:26:35Z | |
dc.date.issued | 2016 | |
dc.identifier.citation | Hartmann, M., Lartillot, O., & Toiviainen, P. (2016). Multi-Scale Modelling of Segmentation : Effect of Music Training and Experimental Task. <i>Music Perception</i>, <i>34</i>(2), 192-217. <a href="https://doi.org/10.1525/MP.2016.34.2.192" target="_blank">https://doi.org/10.1525/MP.2016.34.2.192</a> | |
dc.identifier.other | CONVID_26396560 | |
dc.identifier.other | TUTKAID_72166 | |
dc.identifier.uri | https://jyx.jyu.fi/handle/123456789/52462 | |
dc.description.abstract | While listening to music, people, often
unwittingly, break down musical pieces into
constituent chunks such as verses and choruses.
Music segmentation studies have suggested that
some consensus regarding boundary perception
exists, despite individual differences. However,
neither the effects of experimental task (i.e. realtime
vs annotated segmentation), nor of musicianship
on boundary perception are clear. Our
study assesses musicianship effects and differences
between segmentation tasks. We conducted
a real-time task experiment to collect segmentations
by musicians and non-musicians from 9 musical
pieces; in a second experiment on non-realtime
segmentation, musicians indicated boundaries
and their strength for 6 examples. Kernel
density estimation was used to develop multiscale
segmentation models. Contrary to previous
research, no relationship was found between
boundary strength and boundary indication density,
although this might be contingent on stimuli
and other factors. In line with other studies,
no musicianship effects were found: our results showed high agreement between groups and similar
inter-subject correlations. Also consistent with
previous work, time scales between and 1 and 2
seconds were optimal for combining boundary indications.
In addition, we found effects of task on
number of indications, and a time lag between
tasks dependent on beat length. Also, the optimal
time scale for combining responses increased
when the pulse clarity or event density decreased.
Implications for future segmentation studies are
raised concerning the selection of time scales for
modelling boundary density, and time alignment
between models. | |
dc.language.iso | eng | |
dc.publisher | University of California Press | |
dc.relation.ispartofseries | Music Perception | |
dc.subject.other | music segmentation | |
dc.subject.other | music training | |
dc.subject.other | segmentation task | |
dc.subject.other | segmentation modelling | |
dc.subject.other | musical features | |
dc.title | Multi-Scale Modelling of Segmentation : Effect of Music Training and Experimental Task | |
dc.type | article | |
dc.identifier.urn | URN:NBN:fi:jyu-201612195166 | |
dc.contributor.laitos | Musiikin laitos | fi |
dc.contributor.laitos | Department of Music | en |
dc.contributor.oppiaine | Musiikkitiede | fi |
dc.contributor.oppiaine | Musicology | en |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
dc.date.updated | 2016-12-19T13:15:07Z | |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.description.reviewstatus | peerReviewed | |
dc.format.pagerange | 192-217 | |
dc.relation.issn | 0730-7829 | |
dc.relation.numberinseries | 2 | |
dc.relation.volume | 34 | |
dc.type.version | publishedVersion | |
dc.rights.copyright | © 2016 by the Regents of the University of California. Published in this repository with the kind permission of the publisher. | |
dc.rights.accesslevel | openAccess | fi |
dc.relation.doi | 10.1525/MP.2016.34.2.192 | |
dc.type.okm | A1 | |