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Appl. Sci. 2019, 9(3), 552; https://doi.org/10.3390/app9030552

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

1
Europa-Universität Flensburg, 24943 Flensburg, Germany
2
Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, 20018 San Sebastian, Spain
3
IKERBASQUE, Basque Foundation for Science, 48013 Bilbao, Spain
*
Author to whom correspondence should be addressed.
Received: 30 November 2018 / Accepted: 1 February 2019 / Published: 7 February 2019
(This article belongs to the Section Acoustics and Vibrations)
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Abstract

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. View Full-Text
Keywords: computational ethnomusicology; contrast mining; pattern discovery; outlier detection; anomaly detection; data mining; Native American music computational ethnomusicology; contrast mining; pattern discovery; outlier detection; anomaly detection; data mining; Native American music
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Neubarth, K.; Conklin, D. Identification and Description of Outliers in the Densmore Collection of Native American Music. Appl. Sci. 2019, 9, 552.

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