Open Access Repository

Investigation of unsupervised models for biodiversity assessment

Downloads

Downloads per month over past year

Rama Rao, KSVN, Garg, S ORCID: 0000-0003-3510-2464 and Montgomery, EJ ORCID: 0000-0002-5360-7514 2018 , 'Investigation of unsupervised models for biodiversity assessment', in T Mitrovic and B Xu and X Li (eds.), Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence , Springer, United States, pp. 160-171 , doi: 10.1007/978-3-030-03991-2_17.

[img] PDF (Author's version, post refereeing)
rr_version.pdf | Request a copy
Full text restricted

Abstract

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 Type: Conference Publication
Authors/Creators:Rama Rao, KSVN and Garg, S and Montgomery, EJ
Keywords: ecoacoustics, bioacoustics, audio processing, biodiversity, unsupervised model
Journal or Publication Title: Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence
Publisher: Springer
ISSN: 0302-9743
DOI / ID Number: 10.1007/978-3-030-03991-2_17
Copyright Information:

Copyright 2018 Springer

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP