Integration over song classification replicates: song variant analysis in the hihi

Ranjard, Louis, Withers, Sarah J., Brunton, Dianne H., Ross, Howard A., & Parsons, Stuart (2015) Integration over song classification replicates: song variant analysis in the hihi. The Journal of the Acoustical Society of America, 137(5), pp. 2542-2551.

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Abstract

Human expert analyses are commonly used in bioacoustic studies and can potentially limit the reproducibility of these results. In this paper, a machine learning method is presented to statistically classify avian vocalizations. Automated approaches were applied to isolate bird songs from long field recordings, assess song similarities, and classify songs into distinct variants. Because no positive controls were available to assess the true classification of variants, multiple replicates of automatic classification of song variants were analyzed to investigate clustering uncertainty. The automatic classifications were more similar to the expert classifications than expected by chance. Application of these methods demonstrated the presence of discrete song variants in an island population of the New Zealand hihi (Notiomystis cincta). The geographic patterns of song variation were then revealed by integrating over classification replicates. Because this automated approach considers variation in song variant classification, it reduces potential human bias and facilitates the reproducibility of the results.

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ID Code: 86193
Item Type: Journal Article
Refereed: Yes
Keywords: Agroacoustics, Cluster analysis, Artificial neural networks, Learning, Acoustical measurements
DOI: 10.1121/1.4919329
ISSN: 1520-8524
Divisions: Current > Schools > School of Earth, Environmental & Biological Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 Acoustical Society of America
Deposited On: 10 Aug 2015 02:10
Last Modified: 12 Aug 2015 22:47

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