Assistive classification for improving the efficiency of avian species richness surveys

, , , , & (2015) Assistive classification for improving the efficiency of avian species richness surveys. In Cao, L, Kwok, J, Pasi, G, Zaiane, O, Gaussier, E, & Gallinari, P (Eds.) Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015). Institute of Electrical and Electronics Engineers Inc., United States of America, pp. 1015-1020.

[img] Published Version (PDF 631kB)
Assistive Classification for Improving the Efficiency of Avian Species Richness Surveys.pdf.
Administrators only | Request a copy from author

View at publisher

Description

Avian species richness surveys, which measure the total number of unique avian species, can be conducted via remote acoustic sensors. An immense quantity of data can be collected, which, although rich in useful information, places a great workload on the scientists who manually inspect the audio. To deal with this big data problem, we calculated acoustic indices from audio data at a one-minute resolution and used them to classify one-minute recordings into five classes. By filtering out the non-avian minutes, we can reduce the amount of data by about 50% and improve the efficiency of determining avian species richness. The experimental results show that, given 60 one-minute samples, our approach enables to direct ecologists to find about 10% more avian species.

Impact and interest:

2 citations in Scopus
0 citations in Web of Science®
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® 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 the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 89566
Item Type: Chapter in Book, Report or Conference volume (Conference contribution)
ORCID iD:
Zhang, Liangorcid.org/0000-0002-6090-4058
Towsey, Michaelorcid.org/0000-0002-8246-7151
Zhang, Jinglanorcid.org/0000-0001-6459-2963
Roe, Paulorcid.org/0000-0002-4892-1509
Measurements or Duration: 6 pages
Keywords: acoustic indices, acoustic sensor data, avian species richness, classification
DOI: 10.1109/DSAA.2015.7344892
ISBN: 978-1-4673-8272-4
Pure ID: 32797923
Divisions: Past > Institutes > Institute for Future Environments
Past > QUT Faculties & Divisions > Science & Engineering Faculty
Copyright Owner: Consult author(s) regarding copyright matters
Copyright Statement: This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au
Deposited On: 27 Oct 2015 23:28
Last Modified: 07 Feb 2025 20:59