Madanayake, Adikarige, Sankupellay, Mangalam, and Lee, Ickjai (2020) Profiling the natural environment using acoustics: long-term environment monitoring through cluster structure. In: Proceedings of the 3rd International Conference on Software Engineering and Information Management. pp. 74-78. From: ICSIM'20: 3rd International Conference on Software Engineering and Information Management, 12-15 January 2020, Sydney, NSW, Australia.

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Abstract

Eco-acoustic recordings of the natural environment are becoming an increasingly important technique for ecologists to monitor and interpret long-term terrestrial ecosystems. Visualisation has been a popular approach to analyse short-term eco-acoustic recordings, but it is practically not feasible for long-term monitoring. Unsupervised machine learning could be a solid candidate to find clustering structures within this long-term eco-acoustic data, and this paper investigates if unsupervised machine learning is able to find any clustering structural difference around an important environmental event, in particular with k-means clustering. Experimental results reveal that there are clear clustering structural changes in general geophony and biophony sounds before and after a bushfire in our study region which indicates that clustering approaches could be used to identify important environmental events.

Item ID:	63011
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-4503-7690-7
Keywords:	Long-term eco-acoustics, Summary indices, k-means clustering, Silhouette analysis, Environmental monitoring
Related URLs:	https://dl.acm.org/doi/proceedings/10.11...
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Date Deposited:	20 Jul 2020 23:45
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery @ 80% 41 ENVIRONMENTAL SCIENCES > 4102 Ecological applications > 410299 Ecological applications not elsewhere classified @ 20%
SEO Codes:	89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890299 Computer Software and Services not elsewhere classified @ 100%
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