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.
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.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
|Item Type:||Journal Article|
|Keywords:||Agroacoustics, Cluster analysis, Artificial neural networks, Learning, Acoustical measurements|
|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|
Repository Staff Only: item control page