Detecting marine mammal vocalizations in underwater acoustic environments and classifying them to species level is typically an arduous manual analysis task for skilled bioacousticians. In recent years, machine learning and other automated algorithms have been explored for quickly detecting and classifying all sound sources in an ambient acoustic environment, but many of these still require a large training dataset compiled through time-intensive manual pre-processing. Here, an application of the signal decomposition technique Empirical Mode Decomposition (EMD) is presented, which does not require a priori knowledge and quickly detects all sound sources in a given recording. The EMD detection process extracts the possible signals in a dataset for minimal quality control post-processing before moving onto the second phase: the EMD classification process. The EMD classification process uniquely identifies and labels most sound sources in a given environment. Thirty-five recordings containing different marine mammal species and mooring hardware noises were tested with the new EMD detection and classification processes. Ultimately, these processes can be applied to acoustic index development and refinement.
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December 2018
December 07 2018
An Empirical Mode Decomposition-based detection and classification approach for marine mammal vocal signals
Kerri D. Seger;
Kerri D. Seger
a)
University of New Hampshire, School of Marine Science and Ocean Engineering
, 24 Colovos Road, Durham, New Hampshire 03824, USA
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Mahdi H. Al-Badrawi;
Mahdi H. Al-Badrawi
University of New Hampshire, Electrical and Computer Engineering
, 33 Academic Way, Durham, New Hampshire 03824, USA
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Jennifer L. Miksis-Olds;
Jennifer L. Miksis-Olds
University of New Hampshire, School of Marine Science and Ocean Engineering
, 24 Colovos Road, Durham, New Hampshire 03824, USA
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Nicholas J. Kirsch;
Nicholas J. Kirsch
University of New Hampshire, Electrical and Computer Engineering
, 33 Academic Way, Durham, New Hampshire 03824, USA
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Anthony P. Lyons
Anthony P. Lyons
University of New Hampshire, Center for Coastal and Ocean Mapping
, 24 Colovos Road, Durham, New Hampshire 03824, USA
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a)
Electronic mail: [email protected]
J. Acoust. Soc. Am. 144, 3181–3190 (2018)
Article history
Received:
March 31 2018
Accepted:
October 16 2018
Citation
Kerri D. Seger, Mahdi H. Al-Badrawi, Jennifer L. Miksis-Olds, Nicholas J. Kirsch, Anthony P. Lyons; An Empirical Mode Decomposition-based detection and classification approach for marine mammal vocal signals. J. Acoust. Soc. Am. 1 December 2018; 144 (6): 3181–3190. https://doi.org/10.1121/1.5067389
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