NDLI: Automatic event detection for long-term monitoring of hydrophone data

Content Provider	IEEE Xplore Digital Library
Author	Sattar, F. Driessen, P.F. Tzanetakis, G. Ness, S.R. Page, W.H.
Copyright Year	2011
Description	Author affiliation: Department of Computer Science, University of Victoria, Canada (Tzanetakis, G.; Ness, S.R.) \|\| Department of Electrical and Computer Eng., University of Victoria, Canada (Driessen, P.F.) \|\| NEPTUNE Canada, University of Victoria, Canada (Sattar, F.) \|\| Institute of Food Nutrition and Human Health, Massey University, New Zealand (Page, W.H.)
Abstract	In this paper, we propose an efficient method for long-term monitoring of a wide variety of marine mammals and human related activities using hydrophone data. The proposed method uses a combination of a two-stage denoising process followed by a new event detection function that estimates temporal predictability. The detection function utilizes long-term and short-term predictions in order to detect various acoustic events from the background noise. The first stage of the denoising process uses temporal decomposition via Empirical Mode Decomposition to improve the correct detection rate, while the second stage uses Wavelet Packet spectral decomposition to reduce the false detection rate. Applied to event detection in NEPTUNE hydrophone recordings, the method demonstrates an accuracy of 95% and an F-measure of 94%.
Starting Page	668
Ending Page	674
File Size	613990
Page Count	7
File Format	PDF
ISBN	9781457702525
ISSN	21545952
e-ISBN	9781457702532
e-ISBN	9781457702518
DOI	10.1109/PACRIM.2011.6032973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-23
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wavelet packets Sonar equipment Entropy Event detection Monitoring
Content Type	Text
Resource Type	Article

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