Mining Characteristic Patterns for Comparative Music Corpus Analysis
dc.contributor.author | Neubarth, Kerstin | |
dc.contributor.author | Conklin, Darrell | |
dc.date.accessioned | 2020-04-23T18:32:12Z | |
dc.date.available | 2020-04-23T18:32:12Z | |
dc.date.issued | 2020-03-14 | |
dc.identifier | doi: 10.3390/app10061991 | |
dc.identifier.citation | Applied Sciences 10(6) : (2020) // Article ID 1991 | es_ES |
dc.identifier.issn | 2076-3417 | |
dc.identifier.uri | http://hdl.handle.net/10810/42878 | |
dc.description.abstract | A core issue of computational pattern mining is the identification of interesting patterns. When mining music corpora organized into classes of songs, patterns may be of interest because they are characteristic, describing prevalent properties of classes, or because they are discriminant, capturing distinctive properties of classes. Existing work in computational music corpus analysis has focused on discovering discriminant patterns. This paper studies characteristic patterns, investigating the behavior of different pattern interestingness measures in balancing coverage and discriminability of classes in top k pattern mining and in individual top ranked patterns. Characteristic pattern mining is applied to the collection of Native American music by Frances Densmore, and the discovered patterns are shown to be supported by Densmore’s own analyses. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
dc.subject | pattern discovery | es_ES |
dc.subject | characteristic pattern | es_ES |
dc.subject | discriminant pattern | es_ES |
dc.subject | music corpus analysis | es_ES |
dc.subject | computational ethnomusicolog | es_ES |
dc.subject | Native American music | es_ES |
dc.title | Mining Characteristic Patterns for Comparative Music Corpus Analysis | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.date.updated | 2020-03-27T14:55:24Z | |
dc.rights.holder | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/) | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/2076-3417/10/6/1991/ | es_ES |
dc.identifier.doi | 10.3390/app10061991 | |
dc.departamentoes | Ciencia de la computación e inteligencia artificial | |
dc.departamentoeu | Konputazio zientziak eta adimen artifiziala |
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Except where otherwise noted, this item's license is described as © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)