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Descriptive rule discovery in folk music

Neubarth, Kerstin (2014) Descriptive rule discovery in folk music. Master of Science by Research (MScRes) thesis, University of Kent,. (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:50441)

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Abstract

A well-known problem in descriptive rule discovery is the large number of rules that may be discovered, making it difficult to gain an overview of the domain, identify interesting rules and understand relations between rules. This thesis extends association rule discovery to extracting rules of different association categories, in the context of computational folk music analysis. Folk music research has an established tradition of analysing relations between folk music genres, geographical regions and musical characteristics, corresponding to associations of different direction, different strength and both positive and negative associations, and relating associations at different hierarchical levels of genres or regions.

The method is applied to the Cancionero Vasco, an iconic collection of Basque folk music. The corpus contains 1902 folk tunes encoded in symbolic format and annotated with genre and region information. The annotation vocabulary is organised in two ontologies, which define hierarchical relations among genres and among regions. Music content is represented by musicologically motivated global features. The corpus is mined for content–genre, content–region and genre–region associations. Discovered rules and rule groups, labelled with association categories, can be translated into statements similar to those in folk music surveys, and individual rules confirm and extend previously stated observations about Basque folk music.

Item Type: Thesis (Master of Science by Research (MScRes))
Thesis advisor: Johnson, Colin
Uncontrolled keywords: data mining association rule discovery computational folk music analysis
Subjects: Q Science > QA Mathematics (inc Computing science)
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science
Depositing User: Users 1 not found.
Date Deposited: 09 Sep 2015 15:00 UTC
Last Modified: 01 Aug 2019 10:39 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50441 (The current URI for this page, for reference purposes)
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