Sankupellay, M., Towsey, M., Truskinger, A., and Roe, P. (2015) Visual fingerprints of the acoustic environment: the use of acoustic indices to characterise natural habitats. In: Proceedings of the International Symposium on Big Data Visual Analytics. pp. 1-8. From: BDVA 2015: International Symposium on Big Data Visual Analytics, 22-25 September 2015, Hobart, TAS, Australia.

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Abstract

Acoustic recordings play an increasingly important role in monitoring terrestrial environments. However, due to rapid advances in technology, ecologists are accumulating more audio than they can listen to. Our approach to this big-data challenge is to visualize the content of long-duration audio recordings by calculating acoustic indices. These are statistics which describe the temporal-spectral distribution of acoustic energy and reflect content of ecological interest. We combine spectral indices to produce false-color spectrogram images. These not only reveal acoustic content but also facilitate navigation. An additional analytic challenge is to find appropriate descriptors to summarize the content of 24-hour recordings, so that it becomes possible to monitor long-term changes in the acoustic environment at a single location and to compare the acoustic environments of different locations. We describe a 24-hour 'acoustic-fingerprint' which shows some preliminary promise.

Item ID:	46050
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-4673-7343-2
Keywords:	visualisation of acoustic data, soundscape ecology, self-organising maps, acoustic environment
Funders:	Queensland University of Technology (QUT)
Projects and Grants:	QUT Samford Ecological Research Facility
Date Deposited:	18 Oct 2016 03:24
FoR Codes:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 70% 08 INFORMATION AND COMPUTING SCIENCES > 0806 Information Systems > 080602 Computer-Human Interaction @ 30%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 50% 96 ENVIRONMENT > 9699 Other Environment > 969999 Environment not elsewhere classified @ 50%
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