An Empirical Mode Decomposition (EMD)-Based Detection and Classification Approach for Marine Mammal Vocal Signals

TitleAn Empirical Mode Decomposition (EMD)-Based Detection and Classification Approach for Marine Mammal Vocal Signals
Publication TypeJournal Article
Year2018
AuthorsSeger, KD, Al-Badrawi, MH, Miksis-Olds, JL, Kirsch, NJ, Lyons, AP
JournalJournal of the Acoustical Society of America
Volume144,6
Pages3181-3190
Date PublishedDecember 7
PublisherAcoustical Society of America

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.

Publication Linkhttps://asa.scitation.org/doi/10.1121/1.5067389?af=R
DOI10.1121/1.5067389
Refereed DesignationRefereed
Check When Not CCOM Publication