An empirical mode decomposition based hidden Markov model approach for detection of Bryde’s whale pulse calls

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dc.contributor.authorOgundile, O. O.
dc.contributor.authorUsman, A. M.
dc.contributor.authorVersfeld, D. J. J
dc.date.accessioned2021-05-07T09:58:31Z
dc.date.available2021-05-07T09:58:31Z
dc.date.issued2020-02-11
dc.descriptionA journal articleen_US
dc.description.abstractThis 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.en_US
dc.description.sponsorshipNational Research Foundation of South Africa (116036)en_US
dc.identifier.citationOgundile, 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/10.1121/10.0000717en_US
dc.identifier.urihttps://doi.org/10.1121/10.0000717
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/5134
dc.language.isoen_USen_US
dc.publisherThe journal of the Acoustical Society of Americaen_US
dc.relation.ispartofseries147;2
dc.subjectEmpirical mode decomposition (EMD)en_US
dc.subjectFeature Extractionen_US
dc.subjectHidden Markov model (HMM)en_US
dc.subjectLinear predictive coefficient (LPC)en_US
dc.subjectMachine Learningen_US
dc.subjectMel-scale frequency cepstral coefficient (MFCC)en_US
dc.titleAn empirical mode decomposition based hidden Markov model approach for detection of Bryde’s whale pulse callsen_US
dc.typeArticleen_US

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