An empirical mode decomposition based hidden Markov model approach for detection of Bryde’s whale pulse calls
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Date
2020-02-11
Journal Title
Journal ISSN
Volume Title
Publisher
The journal of the Acoustical Society of America
Abstract
This letter proposes an empirical mode decomposition (EMD) based hidden
Markov model (HMM) approach for the detection of mysticetes’ pulse calls such as the
Bryde’s whales. The HMM detection capabilities depend on the deployed feature extraction
(FE) technique. The EMD is proposed as a performance efficient alternative to the popular
Mel-scale frequency cepstral coefficient (MFCC) and linear predictive coefficient (LPC) FE
techniques. The amplitude modulation–frequency modulation components derived from
the EMD process are modified to form feature vectors for the HMM. Also, the ensemble
EMD (EEMD) is adapted in a similar way as the EMD. These proposed EMD-HMM
and EEMD-HMM approaches achieved better performance in comparison to the
MFCC-HMM and LPC-HMMapproaches.
Description
A journal article
Keywords
Empirical mode decomposition (EMD), Feature Extraction, Hidden Markov model (HMM), Linear predictive coefficient (LPC), Machine Learning, Mel-scale frequency cepstral coefficient (MFCC)
Citation
Ogundile, O. O., Usman, A.M., & Versfeld, D.J.J (2020): An empirical mode decomposition based hidden Markov model approach for detection of Bryde’s whale pulse calls. The journal of the Acoustical Society of America, Vol 147, no 2; pp 125-131, Published by The Acoustical Society of America. Available online at https://asa.scitation.org/doi/10.1121/10.0000717