Social network analysis of an acoustic environment: the use of visualised data to characterise natural habitats

Wang, Junling, Sankupellay, Mangalam, Konovalov, Dmitry, Towsey, Michael, and Roe, Paul (2019) Social network analysis of an acoustic environment: the use of visualised data to characterise natural habitats. In: Proceedings of the International Conference on Digital Image Computing. pp. 366-372. From: DICTA 2019: International Conference on Digital Image Computing: Techniques and Applications, 2-4 December 2019, Perth, WA, Australia.

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Abstract

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.

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.

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).

Conclusion: Social network maps and summary values provide insight into acoustic changes within an environment visually and quantitatively.

Item ID: 61470
Item Type: Conference Item (Research - E1)
ISBN: 978-1-7281-3857-2
Copyright Information: © 2019 IEEE
Date Deposited: 22 Jan 2020 01:21
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%
SEO Codes: 96 ENVIRONMENT > 9608 Flora, Fauna and Biodiversity > 960805 Flora, Fauna and Biodiversity at Regional or Larger Scales @ 100%
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