Contrast Pattern Mining in Folk Music Analysis

Neubarth, Kerstin; Conklin, Darrell

doi:10.1007/978-3-319-25931-4_15

Cited by 11 publications

(6 citation statements)

References 60 publications

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“…Within computational ethnomusicology, our method builds on research in contrast pattern mining [55]. In particular, it complements work on antipatterns, i.e., patterns over-represented in the background or anticorpus [35]: while the earlier study illustrated that exceptions to antipatterns might reveal erroneous or ambiguous class labelling (class outliers), here songs described by infrequent patterns reveal properties which are unusual within their class (attribute outliers).…”

Section: Discussionmentioning

confidence: 99%

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Identification and Description of Outliers in the Densmore Collection of Native American Music

Neubarth

Conklin

2019

Applied Sciences

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This paper presents a method for outlier detection in structured music corpora. Given a music collection organised into groups of songs, the method discovers contrast patterns which are significantly infrequent in a group. Discovered patterns identify and describe outlier songs exhibiting unusual properties in the context of their group. Applied to the collection of Native American music collated by Frances Densmore (1867–1957) during fieldwork among several North American tribes, and employing Densmore’s music content descriptors, the proposed method successfully discovers a concise set of patterns and outliers, many of which correspond closely to observations about tribal repertoires and songs presented by Densmore.

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Section: Discussionmentioning

confidence: 99%

“…Number Percent Melodic 3,4,5,8,9,12,13,14,15,16,18,19,22,23,24,25,28,31,33,34,35,36,37,38,39,42,43,44,46,47,48,49,50,51,52,53,54,55,56,58,59,60,61,62,64,65,66,67,72,73,76,78,80,81,83,84,85,…”

Section: Structure Serial Number Of Songsmentioning

confidence: 99%

Identification and Description of Outliers in the Densmore Collection of Native American Music

Neubarth

Conklin

2019

Applied Sciences

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“…While the majority of contrast set research concerned medical data [12,13,14,22], the different areas of application like aircraft incidents [23], software crashes [24], or folk music [25] were also investigated in the literature.…”

Section: Related Workmentioning

confidence: 99%

Separate and conquer heuristic allows robust mining of contrast sets from various types of data

Gudyś¹,

Sikora²,

Wróbel³

2022

Preprint

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Identifying differences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industry, or economics.In the paper we present RuleKit-CS, an algorithm for contrast set mining based on a sequential covering -a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme allow generating contrast sets describing same examples with different attributes, unlike the standard sequential covering. The ability to identify contrast sets in regression and survival data sets, the feature not provided by the existing algorithms, further extends the usability of RuleKit-CS.Experiments on wide range of data sets confirmed RuleKit-CS to be a useful tool for discovering differences between defined groups. The algorithm is a part of the RuleKit suite available at GitHub under GNU AGPL 3 licence (https://github.com/adaa-polsl/RuleKit).

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“…In 2016, the authors of [141] use CPs to distinguish groups of pieces within a music corpus. The authors mine different types of patterns on different datasets without methodological support for this decision.…”

Section: Classificationmentioning

confidence: 99%

A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges

2020

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Supervised classification based on Contrast Patterns (CP) is a trending topic in the pattern recognition literature, partly because it contains an important family of both understandable and accurate classifiers. In this paper, we survey 105 articles and provide an in-depth review of CP-based supervised classification and its applications. Based on our review, we present a taxonomy of the existing application domains of CP-based supervised classification, and a scientometric study. We also discuss potential future research opportunities.

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Contrast Pattern Mining in Folk Music Analysis

Cited by 11 publications

References 60 publications

Identification and Description of Outliers in the Densmore Collection of Native American Music

Identification and Description of Outliers in the Densmore Collection of Native American Music

Separate and conquer heuristic allows robust mining of contrast sets from various types of data

A Review of Supervised Classification based on Contrast Patterns: Applications, Trends, and Challenges

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