Detection of Bryde’s Whale Short Pulse calls using Time Domain Features with Hidden Markov Models

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dc.contributor.authorBabalola, O. P.
dc.contributor.authorUsman, A. M.
dc.contributor.authorOgundile, O. O.
dc.contributor.authorVersfeld, D. J. J.
dc.date.accessioned2021-03-31T14:25:34Z
dc.date.available2021-03-31T14:25:34Z
dc.date.issued2021-03
dc.descriptionJournal articleen_US
dc.description.abstractPassive acoustic monitoring (PAM) is generally used to extract acoustic signals produced by cetaceans. However, the large data volume from the PAM process is better analyzed using an automated technique such as the hidden Markov models (HMM). In this paper, the HMM is used as a detection and classification technique due to its robustness and low time complexity. Nonetheless, certain parameters, such as the choice of features to be extracted from the signal, the frame duration, and the number of states affect the performance of the model. The results show that HMM exhibits best performances as the number of states increases with short frame duration. However, increasing the number of states creates more computational complexity in the model. The inshore Bryde’s whales produce short pulse calls with distinct signal features, which are observable in the time-domain. Hence, a time-domain feature vector is utilized to reduce the complexity of the HMM. Simulation results also show that average power as a time-domain feature vector provides the best performance compared to other feature vectors for detecting the short pulse call of inshore Bryde’s whales based on the HMM technique. More so, the extracted features such as the average power, mean, and zero-crossing rate, are combined to form a single 3-dimensional vector (PaMZ). The PaMZ-HMM shows improved performance and reduced complexity over existing feature extraction techniques such as Mel-scale frequency cepstral coefficients (MFCC) and linear predictive coding (LPC). Thus, making the PaMZ-HMM suitable for real-time detection.en_US
dc.identifier.citationBabalola, O. P., Usman, A.M., Ogundile, O.O., & Versfeld, D.J.J (2021): Detection of Bryde’s Whale Short Pulse calls using Time Domain Features with Hidden Markov Models. SAIEE Africa Research Journal, Vol 112, no 1; pp 15-23, Published by the South African Institute of Electrical Engineers (SAIEE). Available online at 10.23919/SAIEE.2021.9340533en_US
dc.identifier.urihttp://hdl.handle.net/123456789/4646
dc.language.isoenen_US
dc.publisherthe South African Institute of Electrical Engineers (SAIEE).en_US
dc.relation.ispartofseries112;1
dc.subjectAcoustic signalen_US
dc.subjectBryde's whalesen_US
dc.subjectHidden Markov modelen_US
dc.subjectPassive acoustic monitoringen_US
dc.subjecttime-domain featuresen_US
dc.titleDetection of Bryde’s Whale Short Pulse calls using Time Domain Features with Hidden Markov Modelsen_US
dc.typeArticleen_US

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