@conference {58034,
	title = {Social Network Analysis of an Acoustic Environment: The Use of Visualised Data to Characterise Natural Habitats},
	year = {2019},
	abstract = {<p>(1) Background: Ecologists use acoustic recordings for long term environmental monitoring. However, as audio recordings are opaque, obtaining meaningful information from them is a challenging task. Calculating summary indices from recordings is one way to reduce the size of audio data, but the amount of information of summary indices is still too big. (2) Method: In this study we explore the application of social network analysis to visually and quantitatively model acoustic changes. To achieve our aim, we clustered summary indices using two algorithms, and the results were used to generate network maps. (3) Results and Discussion: The network maps allowed us to visually perceive acoustic changes in a day and to visually compare one day to another. To enable quantitative comparison, we also calculated summary values from the social network maps, including Gini coefficient (an economical concept adopted to estimate how unevenly the occurrences are distributed). (4) Conclusion: Social network maps and summary values provide insight into acoustic changes within an environment visually and quantitatively.</p>
},
	doi = {10.1109/DICTA47822.201910.1109/DICTA47822.2019.8945963},
	url = {https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8943071https://ieeexplore.ieee.org/document/8945963/},
	author = {Wang, Junling and Sankupellay, Mangalam and Konovalov, Dmitry and Towsey, Michael and Roe, Paul}
}
