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Machine Learning Classification of Regional Swiss Yodel Styles Based on Their Melodic Attributes

descriptionPublicationkeyboard_double_arrow_right Article 01 Jan 2021 Switzerland SAGE PublicationsMusic & Science, volume 4, page 205,920,432,110,044 (issn: 2059-2043, eissn: 2059-2043,

Authors: Yannick Wey; Cornelia Metzig;

doi: 10.1177/20592043211004497 , 10.5281/zenodo.6602665 , 10.5281/zenodo.6602666

Machine Learning Classification of Regional Swiss Yodel Styles Based on Their Melodic Attributes

- Summary
- Subjects
- References
  (43)
- Metrics

Abstract

A classification of wordless yodel melodies from five different regions in Switzerland was made. For our analysis, we used a total of 217 yodel tunes from five regions, which can be grouped into two larger regions, central and north-eastern Switzerland. The results show high accuracy of classification, therefore confirming the existence of regional differences in yodel melodies. The most salient features, such as rhythmic patterns or intervals, demonstrate some of the key differences in pairwise comparisons, which can be confirmed by a postanalysis survey of the relevant scores.

+ ID der Publikation: hslu_83278 + Art des Beitrages: Wissenschaftliche Medien + Jahrgang: 4 + Sprache: Englisch + Letzte Aktualisierung: 2022-06-01 13:58:19

Country

Switzerland

Related Organizations

Queen Mary University of London
United Kingdom
Zentral und Hochschulbibliothek Luzern
Switzerland
FACHHOCHSCHULE ZENTRALSCHWEIZ - HOCHSCHULE LUZERN
Switzerland
Lucerne University of Applied Sciences and Arts
Switzerland

Subjects by Vocabulary

Microsoft Academic Graph classification: Melody Computer science computer.software_genre Folk music business.industry Statistical classification Artificial intelligence business computer Natural language processing

Keywords

yodel, ethnomusicology, Psychology, M1-5000, folk music, computational musicology, Classification, BF1-990, machine learning, Swittzerland, Music

43 references, page 1 of 5

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Conklin, D. (2013). Multiple viewpoint systems for music classification. Journal of New Music Research, 42(1), 19-26.

Conklin, D., & Anagnostopoulou, C. (2011). Comparative pattern analysis of Cretan folk songs. Journal of New Music Research, 40(2), 119-125.

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Eerola, T., & Toiviainen, P. (2004). MIDI toolbox: MATLAB tools for music research. University of Jyva¨skyla¨, Department of Music. [OpenAIRE]

Eidgeno¨ ssischer Jodlerverband. (2010). Lebendiges Schweizer Brauchtum 1910-2010. Eidgeno¨ssischer Jodlerverband.

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average