Assistive classification for improving the efficiency of avian species richness surveys

Zhang, Liang, Towsey, Michael, Eichinski, Philip, Zhang, Jinglan, & Roe, Paul (2015) Assistive classification for improving the efficiency of avian species richness surveys. In Data Science and Advanced Analystics, 19 - 21 October 2015, Paris, France. (In Press)

Abstract

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:

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ID Code: 89566
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: classification, avian species richness, acoustic sensor data, acoustic indices
Subjects: Australian and New Zealand Standard Research Classification > ENVIRONMENTAL SCIENCES (050000) > ENVIRONMENTAL SCIENCE AND MANAGEMENT (050200) > Conservation and Biodiversity (050202)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 [Please consult the author]
Deposited On: 27 Oct 2015 23:28
Last Modified: 18 Dec 2015 19:54

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