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Contrast Pattern Mining in Folk Music Analysis

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Computational Music Analysis

Abstract

Comparing groups in data is a common theme in corpus-level music analysis and in exploratory data mining. Contrast patterns describe significant differences between groups. This chapter introduces the task and techniques of contrast pattern mining and reviews work in quantitative and computational folk music analysis as mining for contrast patterns. Three case studies are presented in detail to illustrate different pattern representations, datasets and groupings of folk music corpora, and pattern mining methods: subgroup discovery of global feature patterns in European folk music, emerging pattern mining of sequential patterns in Cretan folk tunes, and association rule mining of positive and negative patterns in Basque folk music. While this chapter focuses on examples in folk music analysis, the concept of contrast patterns offers opportunities for computational music analysis more generally, which can draw on both musicological traditions of quantitative comparative analysis and research in contrast data mining.

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Authors and Affiliations

  1. Canterbury Christ Church University, Canterbury, UK

    Kerstin Neubarth

  2. Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, San Sebastián, Spain

    Darrell Conklin

  3. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

    Darrell Conklin

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  1. Kerstin Neubarth
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Correspondence to Kerstin Neubarth .

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  1. Dept. of Arch., Design & Media Tech, Aalborg University, Aalborg, Denmark

    David Meredith

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Neubarth, K., Conklin, D. (2016). Contrast Pattern Mining in Folk Music Analysis. In: Meredith, D. (eds) Computational Music Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-25931-4_15

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