Ogundile, O. O.Usman, A. M.Versfeld, D. J. J2021-05-072021-05-072020-02-11Ogundile, 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/10.1121/10.0000717https://doi.org/10.1121/10.0000717https://uilspace.unilorin.edu.ng/handle/20.500.12484/5134A journal articleThis 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-USEmpirical mode decomposition (EMD)Feature ExtractionHidden Markov model (HMM)Linear predictive coefficient (LPC)Machine LearningMel-scale frequency cepstral coefficient (MFCC)Article