The navigation and visualisation of environmental audio using zooming spectrograms
Towsey, Michael, Truskinger, Anthony, & Roe, Paul (2015) The navigation and visualisation of environmental audio using zooming spectrograms. In Dy, J, Wu, X, Cui, P, Zhou, Z H, Xiong, H, Aggarwal, C, et al. (Eds.) Proceedings of the 2015 IEEE 15th International Conference on Data Mining Workshops. Institute of Electrical and Electronics Engineers Inc., United States of America, pp. 788-797.
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Description
Acoustic recordings play an increasingly important role in monitoring terrestrial and aquatic environments. However, rapid advances in technology make it possible to accumulate thousands of hours of recordings, more than ecologists can ever listen to. Our approach to this big-data challenge is to visualize the content of long-duration audio recordings on multiple scales, from minutes, hours, days to years. The visualization should facilitate navigation and yield ecologically meaningful information prior to listening to the audio. To construct images, we calculate acoustic indices, statistics that describe the distribution of acoustic energy and reflect content of ecological interest. We combine various indices to produce false-color spectrogram images that reveal acoustic content and facilitate navigation. The technical challenge we investigate in this work is how to navigate recordings that are days or even months in duration. We introduce a method of zooming through multiple temporal scales, analogous to Google Maps. However, the “landscape” to be navigated is not geographical and not therefore intrinsically visual, but rather a graphical representation of the underlying audio. We describe solutions to navigating spectrograms that range over three orders of magnitude of temporal scale. We make three sets of observations: 1. We determine that at least ten intermediate scale steps are required to zoom over three orders of magnitude of temporal scale; 2. We determine that three different visual representations are required to cover the range of temporal scales; 3. We present a solution to the problem of maintaining visual continuity when stepping between different visual representations. Finally, we demonstrate the utility of the approach with four case studies.
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| ID Code: | 91822 | ||||
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| Item Type: | Chapter in Book, Report or Conference volume (Conference contribution) | ||||
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| Measurements or Duration: | 10 pages | ||||
| Event Title: | Environmental Acoustic Data Mining [EADM] held in conjunction with 2015 IEEE International Conference on Data Mining [ICDM] | ||||
| Event Dates: | 2015-11-14 - 2015-11-17 | ||||
| Event Location: | UNSPECIFIED | ||||
| DOI: | 10.1109/ICDMW.2015.118 | ||||
| ISBN: | 978-1-4673-8492-6 | ||||
| Pure ID: | 32803978 | ||||
| Divisions: | Past > Institutes > Institute for Future Environments Past > QUT Faculties & Divisions > Science & Engineering Faculty |
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| Copyright Owner: | Copyright 2015 IEEE | ||||
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| Deposited On: | 14 Jan 2016 09:56 | ||||
| Last Modified: | 29 Sep 2026 05:48 |
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