Journal article
Integration over song classification replicates: song variant analysis in the hihi
Journal of the Acoustical Society of America, Vol.137(5), pp.2542-2551
2015
PMID: 25994687
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.
Details
- Title
- Integration over song classification replicates: song variant analysis in the hihi
- Authors
- Louis Ranjard (Author) - University of AucklandSarah J Withers (Author) - University of AucklandDianne H Brunton (Author) - Massey UniversityHoward A Ross (Author) - University of AucklandStuart Parsons (Author) - University of Auckland
- Publication details
- Journal of the Acoustical Society of America, Vol.137(5), pp.2542-2551
- Publisher
- A I P Publishing LLC
- DOI
- 10.1121/1.4919329
- ISSN
- 1520-8524
- PMID
- 25994687
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99575106002621
- Output Type
- Journal article
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- Domestic collaboration
- Web Of Science research areas
- Acoustics
- Audiology & Speech-language Pathology
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Source: InCites