Music information visualization and classical composers discovery: an application of network graphs, multidimensional scaling, and support vector machines

Basit Öğe Kaydı
dc.authorid0000-0003-1852-6433en_US
dc.contributor.authorSeçkin, Aylin
dc.date.accessioned2022-12-13T14:02:45Z
dc.date.available2022-12-13T14:02:45Z
dc.date.issued2022-03-27
dc.description.abstractAbstract: This article illustrates diferent information visualization techniques applied to a database of classical composers and visualizes both the macrocosm of the Common Practice Period and the microcosms of twentieth century classical music. It uses data on personal (composer-to-composer) musical infuences to generate and analyze network graphs. Data on style infuences and composers ‘ecological’ data are then combined to composer-to-composer musical infuences to build a similarity/distance matrix, and a multidimensional scaling analysis is used to locate the relative position of composers on a map while preserving the pairwise distances. Finally, a support-vector machines algorithm is used to generate classifcation maps. This article falls into the realm of an experiment in music education, not musicology. The ultimate objective is to explore parts of the classical music heritage and stimulate interest in discovering composers. In an age ofering either inculcation through lists of prescribed composers and compositions to explore, or music recommendation algorithms that automatically propose works to listen to next, the analysis illustrates an alternative path that might promote the active rather than passive discovery of composers and their music in a less restrictive way than inculcation through prescriptionen_US
dc.fullTextLevelFull Texten_US
dc.identifier.doi10.1007/s11192-022-04331-8
dc.identifier.issn1588-2861
dc.identifier.issn0138-9130
dc.identifier.scopus2-s2.0-85126801049en_US
dc.identifier.urihttps://hdl.handle.net/11411/4748
dc.identifier.urihttps://doi.org/10.1007/s11192-022-04331-8
dc.identifier.wosWOS:000770736800001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.issue5en_US
dc.language.isoenen_US
dc.nationalInternationalen_US
dc.numberofauthors2en_US
dc.pages2277-2311en_US
dc.publisherSPRINGERen_US
dc.relation.ispartofScientometricsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDigital humanitiesen_US
dc.subjectMusicological data visualizationen_US
dc.subjectNetwork graphsen_US
dc.subjectSimilarity indicesen_US
dc.subjectMultidimensional scalingen_US
dc.subjectMusic information retrievalen_US
dc.subjectMusic heritage and educationen_US
dc.titleMusic information visualization and classical composers discovery: an application of network graphs, multidimensional scaling, and support vector machines
dc.typeArticle
dc.volume127en_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Seçkin2022.pdf
Boyut:
2.17 MB
Biçim:
Adobe Portable Document Format
Açıklama:
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.71 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Faculty of Business
Scopus Indexed Publications
Web of Science Indexed Publications