Profiling the natural environment using acoustics: long-term environment monitoring through cluster structure
Conference Publication ResearchOnline@JCUAbstract
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.
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ICSIM'20: 3rd International Conference on Software Engineering and Information Management
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ISBN/ISSN
978-1-4503-7690-7
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Pages Count
5
Location
Sydney, NSW, Australia
Publisher
Association for Computing Machinery
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New York, NY, USA
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DOI
10.1145/3378936.3378946