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Investigation of unsupervised models for biodiversity assessment


Rama Rao, KSVN and Garg, S and Montgomery, J, Investigation of unsupervised models for biodiversity assessment, Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence, 11-14 December 2018, Wellington, New Zealand, Lecture Notes in Computer Science, 11320, pp. 160-171. ISSN 0302-9743 (2018) [Refereed Conference Paper]

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Copyright 2018 Springer

DOI: doi:10.1007/978-3-030-03991-2_17


Significant animal species loss has been observed in recent decades due to habitat destruction, which puts at risk environmental integrity and biodiversity. Traditional ways of assessing biodiversity are limited in terms of both time and space, and have high cost. Since the presence of animals can be indicated by sound, recently acoustic recordings have been used to estimate species richness. Bioacoustic sounds are typically recorded in habitats for several weeks, so contain a large collection of different sounds. Birds are of particular interest due to their distinctive calls and because they are useful ecological indicators. To assess biodiversity, the task of manually determining how many different types of birds are present in such a lengthy audio is really cumbersome. Towards providing an automated support to this issue, in this paper we investigate and propose a clustering based approach to assist in automated assessment of biodiversity. Our approach first estimates the number of different species and their volumes which are used for deriving a biodiversity index. Experimental results with real data indicates that our proposed approach estimates the biodiversity index value close to the ground truth.

Item Details

Item Type:Refereed Conference Paper
Keywords:ecoacoustics, bioacoustics, audio processing, biodiversity, unsupervised model
Research Division:Information and Computing Sciences
Research Group:Computer vision and multimedia computation
Research Field:Pattern recognition
Objective Division:Environmental Management
Objective Group:Terrestrial systems and management
Objective Field:Terrestrial biodiversity
UTAS Author:Rama Rao, KSVN (Mr Rama Rao Kaluri)
UTAS Author:Garg, S (Dr Saurabh Garg)
UTAS Author:Montgomery, J (Dr James Montgomery)
ID Code:130185
Year Published:2018
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2019-01-15
Last Modified:2020-05-13

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