Decision support for the efficient annotation of bioacoustic events
Acoustic sensors allow scientists to scale environmental monitoring over large spatiotemporal scales. The faunal vocalisations captured by these sensors can answer ecological questions, however, identifying these vocalisations within recorded audio is difficult: automatic recognition is currently intractable and manual recognition is slow and error prone. In this paper, a semi-automated approach to call recognition is presented. An automated decision support tool is tested that assists users in the manual annotation process. The respective strengths of human and computer analysis are used to complement one another. The tool recommends the species of an unknown vocalisation and thereby minimises the need for the memorization of a large corpus of vocalisations. In the case of a folksonomic tagging system, recommending species tags also minimises the proliferation of redundant tag categories.
We describe two algorithms: (1) a “naïve” decision support tool (16%–64% sensitivity) with efficiency of O(n) but which becomes unscalable as more data is added and (2) a scalable alternative with 48% sensitivity and an efficiency ofO(log n). The improved algorithm was also tested in a HTML-based annotation prototype. The result of this work is a decision support tool for annotating faunal acoustic events that may be utilised by other bioacoustics projects.
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|Item Type:||Journal Article|
|Keywords:||Similarity search, Bioacoustics, Annotations, Semi-automated, Decision support, Faunal vocalisation|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > OTHER INFORMATION AND COMPUTING SCIENCES (089900) > Information and Computing Sciences not elsewhere classified (089999)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 Elsevier B.V.|
|Copyright Statement:||NOTICE: this is the author’s version of a work that was accepted for publication in Ecological Informatics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Ecological Informatics, Volume 25, January 2015, DOI: 10.1016/j.ecoinf.2014.10.001|
|Deposited On:||11 Dec 2014 00:55|
|Last Modified:||10 Jan 2017 20:04|
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