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Investigation of unsupervised models for biodiversity assessment
conference contribution
posted on 2023-05-23, 13:52 authored by Rama Rao, KSVN, Saurabh GargSaurabh Garg, Erin MontgomeryErin MontgomerySignificant 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.
History
Publication title
Proceedings of the 31st Australasian Joint Conference on Artificial IntelligenceVolume
11320Editors
T Mitrovic, B Xu, X LiPagination
160-171ISSN
0302-9743Department/School
School of Information and Communication TechnologyPublisher
SpringerPlace of publication
United StatesEvent title
31st Australasian Joint Conference on Artificial IntelligenceEvent Venue
Lecture Notes in Computer Science, 11320Date of Event (Start Date)
2018-12-11Date of Event (End Date)
2018-12-14Rights statement
Copyright 2018 SpringerRepository Status
- Restricted