Profiling the Natural Environment Using Acoustics: Long-Term Environment Monitoring through Cluster Structure

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.