Rapid scanning of spectrograms for efficient identification of bioacoustic events in big data
Truskinger, Anthony, Cottman-Fields, Mark, Johnson, Daniel, & Roe, Paul (2013) Rapid scanning of spectrograms for efficient identification of bioacoustic events in big data. In 2013 IEEE 9th International Conference on eScience (eScience), IEEE, Beijing, China, pp. 270-277.
Acoustic sensing is a promising approach to scaling faunal biodiversity monitoring. Scaling the analysis of audio collected by acoustic sensors is a big data problem. Standard approaches for dealing with big acoustic data include automated recognition and crowd based analysis. Automatic methods are fast at processing but hard to rigorously design, whilst manual methods are accurate but slow at processing. In particular, manual methods of acoustic data analysis are constrained by a 1:1 time relationship between the data and its analysts. This constraint is the inherent need to listen to the audio data. This paper demonstrates how the efficiency of crowd sourced sound analysis can be increased by an order of magnitude through the visual inspection of audio visualized as spectrograms. Experimental data suggests that an analysis speedup of 12× is obtainable for suitable types of acoustic analysis, given that only spectrograms are shown.
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|Item Type:||Conference Paper|
|Keywords:||sensors, acoustic data, spectrograms, big data, big data analysis, crowdsourcing, fast forward|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > COMPUTER SOFTWARE (080300) > Software Engineering (080309)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Computer-Human Interaction (080602)
|Divisions:||Past > Schools > Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Deposited On:||07 Jan 2014 01:54|
|Last Modified:||29 Aug 2014 00:24|
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