Browsing by Author "Ogundile, O. O."
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Item Detection of Bryde’s Whale Short Pulse calls using Time Domain Features with Hidden Markov Models(the South African Institute of Electrical Engineers (SAIEE)., 2021-03) Babalola, O. P.; Usman, A. M.; Ogundile, O. O.; Versfeld, D. J. J.Passive 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.Item Dynamic mode decomposition: A feature extraction technique based hidden Markov model for detection of Mysticetes’ vocalisations(Elsevier, 2021-04-28) Ogundile, O. O.; Usman, A. M.; Babalola, O. P.; Versfeld, D. J. J.The detection and classification of Mysticetes’ vocalisations have evoked the attention of researchers over the years because of their relevance to the marine ecosystem. These vocalisations are gathered employing different passive acoustic monitoring techniques. The vocalisation datasets are accumulated over a period; thus, they are large and cannot be easily analysed manually. Consequently, efficient machine learning (ML) tools such as the hidden Markov models (HMMs) are used extensively to automatically detect and classify these huge vocalisation datasets. As with most ML tools, the detection efficiency of the HMMs depend on the adopted feature extraction technique. Feature extraction techniques such as the Mel-scale frequency cepstral coefficient (MFCC), linear predictive coefficient (LPC), and empirical mode decomposition (EMD) have been adopted with the HMMs to detect different Mysticetes’ vocalisations. This article introduces the method of dynamic mode decomposition (DMD) as a feature extraction technique that can be adopted with the HMMs for the detection of Mysticetes’ vocalisations. The DMD has emerged as a robust tool for analysing the dynamics of non-stationary and non-linear signals. It is a completely data-driven tool that decomposes a signal over a certain period of time into relevant modes. Here, these spatial-temporal modes are reconstructed mathematically to form reliable feature vectors of the decomposed vocalisation signals. The performance of the proposed DMD-HMM detection technique is demonstrated using the acoustic datasets of two different Mysticetes’ vocalisations: Humpback whale songs and Inshore Bryde’s whale short pulse calls. In both species, the proposed DMD-HMM exhibits superior sensitivity and false discovery rate performances as compared to the MFCC-HMM, LPC-HMM, and EMD-HMM detection methods. Likewise, this proposed DMD-HMM detection method can be extended to other Mysticetes’ that produce characteristics sounds.Item 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, 2020-02-11) Ogundile, O. O.; Usman, A. M.; Versfeld, D. J. JThis 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.Item A hidden Markov model with selective time domain feature extraction to detect inshore Bryde's whale short pulse calls(Elsevier, 2020-04-02) Ogundile, O. O.; Usman, A. M.; Versfeld, D. J. JAn Increase in the study of cetaceans' sounds has motivated the development of different automated sound detection and classification techniques. Passive acoustic monitoring (PAM) is extensively used to study these cetaceans' sounds over a period to understand their daily activities within their ecosystem. Using PAM, the gathered sound datasets are usually large and impractical to manually analyse and detect. Thus, hidden Markov models (HMM) is one of the popular tools used to automatically detect and classify these cetaceans' sounds. Nonetheless, HMM rely heavily on the employed feature extraction method such as Mel-scale frequency cepstral coefficients (MFCC) and linear predictive coding (LPC). In most cases, the more reliable the extracted feature vector from the known sound label, the higher the sensitivity of the HMM. Although these aforementioned feature extraction methods are widely used, their design is based on filters and requires windowing, fast Fourier transforms (FFT), and logarithm operations. Consequently, this increases the computational time complexity of the HMM. Here, we describe a selective time domain feature extraction method that can be easily adapted with the HMM. This proposed feature extraction method uses a combination of some simple but robust parameters such as the mean, relative amplitude and relative power/energy (MAP), which are selected based on empirical observations of the call to be detected. The performance of this proposed MAP-HMM was verified using the acoustic dataset of continuous recordings of an inshore Bryde's whale (Balaenoptera) short pulse calls collected in a single site in False bay, South-West of South Africa. Aside from exhibiting a low computational complexity, the proposed MAP-HMM offers superior sensitivity and false discovery rate performances in comparison to the LPCHMM and MFCC-HMM.Item IMPROVED DISTANCE METRIC TECHNIQUE FOR DERIVING SOFT RELIABILITY INFORMATION OVER RAYLEIGH FADING CHANNEL(Nigerian Journal of Technology, 2018) Ogundile, O. O.; Oloyede, M. O.; Osanaiye, O. A.; Aina, F. A.Item Polynomial based Channel Estimation Technique with Sliding Window for M-QAM Systems((IJACSA) International Journal of Advanced Computer Science and Applications, 2016) Ogundile, O. O.; Oloyede, M. O.; Aina, F. A.; Oyewobi, S. S.Pilot Symbol Assisted Modulation (PSAM) channel estimation techniques over Rayleigh fading channels have been analysed in recent years. Fluctuations in the Rayleigh fading channel gain degrades the performance of any modulation scheme. This paper develops and analyses a PSAM Polynomial interpolation technique based on Least Square (LS ) approximations to estimate the Channel State Information (CSI) for M-ary Quadrature Amplitude Modulation (M-QAM) over flat Rayleigh fading channels. A Sliding window approach with pilot symbol adjustment is employed in order to minimize the computational time complexity of the estimation technique. The channel estimation performance, and its computational delay and time complexity is verified for di erent Doppler frequencies ( fd), frame lengths (L), and Polynomial orders (P-orders). Simulation results show that the Cubic Polynomial interpolation gives superior Symbol Error Rate (SER) performance than the Quadratic Polynomial interpolation and higher P-orders, and the performance of the Polynomial estimation techniques degrade with increase in the P-orders.