Mining Characteristic Patterns for Comparative Music Corpus Analysis
Source abstract
Abstract
A core issue of computational pattern mining is the identification of interesting patterns. When mining music corpora organized into classes of songs, patterns may be of interest because they are characteristic, describing prevalent properties of classes, or because they are discriminant, capturing distinctive properties of classes. Existing work in computational music corpus analysis has focused on discovering discriminant patterns. This paper studies characteristic patterns, investigating the behavior of different pattern interestingness measures in balancing coverage and discriminability of classes in top k pattern mining and in individual top ranked patterns. Characteristic pattern mining is applied to the collection of Native American music by Frances Densmore, and the discovered patterns are shown to be supported by Densmore’s own analyses.
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Research context
OpenAlex topics: Music and Audio Processing · Data Mining Algorithms and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Europa-Universität Flensburg, 24943 Flensburg, Germany
Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, 20018 San Sebastian, Spain
IKERBASQUE, Basque Foundation for Science, 48013 Bilbao, Spain
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