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

Ogundile, Olayinka O.; Usman, Ayinde M.; Versfeld, Jaco

doi:10.1121/10.0000717

Cited by 12 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coefficients (LPC) are often used as frequency domain techniques in speech recognition and other applications [49], [50]. More so, both the MFCC and LPC are popular feature extraction algorithms used with the HMM [32], [51]. The MFCC derives feature coefficients by converting the signal from time domain to the Mel frequency scale, which represents the short-term power spectrum of the sound.…”

Section: Feature Extraction For Hmmsmentioning

confidence: 99%

Detection of Bryde's whale short pulse calls using time domain features with hidden Markov models

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Feature Extraction For Hmmsmentioning

confidence: 99%

Detection of Bryde's whale short pulse calls using time domain features with hidden Markov models

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, when the state of space is divided and has a limited number of states, the Hidden Markov Models filter is the best selection to overcome this issue [24]. This filter assumes that the tracking of the next state mainly depends on the current state.…”

Section: Features Detection For Trackingmentioning

confidence: 99%

Interpretation of Spatial Relationships by Objects Tracking in a Complex Streaming Video

Alabid

2021

ECTI-CIT

View full text Add to dashboard Cite

By interpreting spatial relations among objects, many applications such as video surveillance, robotics, and scene understanding systems can be utilized efficiently for different purposes. The vast majority of known models for spatial relationships are carried out with an image. However, due to the advance in technology, a three-dimensional scene became available. For our knowledge, most of the interpreted spatial relations were defined between silent objects in images. A technique for determining the dynamic spatial relation between a moving object and another silent one in a time varying scene is presented here. The spatial relationships were determined by using motion-based object tracking along with hypergraph object-oriented model. Defining the spatial relationship types between a single silent object and a moving human body has applied based on two strategies; determining each object with a bounding box, then comparing the locations of these boxes by applying certain conditional rules. This study identifies some of the spatial relationships in three dimensions of streaming frames, which has carried out by establishing a highly accurate and efficient proposed algorithm. The following relations have been studied; (“direct in front of”, “in front of on the Right/Left”, “direct behind of”, “behind of on the Right/Left”, “to the Right”, “to the Left”, “On”, “Under”, Besides, and “Besides on to the Right/Left”). The experimental results, which have been obtained based on actual indoor streaming frames, show effectiveness and reliable execution of our system

show abstract

“…In some recent works [39], [45], the EMD method was used to extracted features without the HHT applied. In [39], the IMF generated from the EMD were used to obtain feature vectors.…”

Section: Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

“…This new approach is a modern way to carry out unsupervised detection and classification in the timedomain depending entirely on EMD-type processing, eliminating the necessity to apply the Hilbert transform and manual labeling of pre-processed data by an expert. They claimed their approach can be applied to a number of transient sound sources (humpback whale songs, Killer whale whistle, beluga whale whistles).Also, in [45], the generated IMFs from EMD process were used to form feature vectors which were fed into a hidden Markov model (HMM) to detect Bryde's whale pulsed calls.…”

Section: Empirical Mode Decomposition (Emd)mentioning

confidence: 99%

See 1 more Smart Citation

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

2020

Self Cite

View full text Add to dashboard Cite

Cetaceans have elicited the attention of researchers in recent decades due to their importance to the ecosystem and their economic values. They use sound for communication, echolocation and other social activities. Their sounds are highly non-stationary, transitory and range from short to long sounds. Passive acoustic monitoring (PAM) is a popular method used for monitoring cetaceans in their ecosystems. The volumes of data accumulated using PAM are usually big, so they are difficult to analyze using manual inspection. Therefore different techniques with mixed outcomes have been developed for the automatic detection and classification of signals of different cetacean species. So far, no single technique developed is perfect to detect and classify the vocalizations of over 82 known species due to variability in time-frequency, difference in the amplitude among species and within species' vocal repertoire, physical environment, among others. The accuracy of any detector or classifier depends on the technique adopted as well as the nature of the signal to be analyzed. In this article, we review the existing techniques for the automatic detection and classification of cetacean vocalizations. We categorize the surveyed techniques, while emphasizing the advantages and disadvantages of these techniques. The article suggests possible research directions that can improve existing detection and classification techniques. In addition, the article recommends other suitable techniques that can be used to analyze non-linear and non-stationary signals such as the cetaceans' signals. Several research have been dedicated to this topic, however, there is no review of these past results that gives a quick overview in the area of cetacean detection and classification. This review will help researchers and practitioners in the field to make insightful decisions based on their requirements.

show abstract

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

Cited by 12 publications

References 11 publications

Detection of Bryde's whale short pulse calls using time domain features with hidden Markov models

Detection of Bryde's whale short pulse calls using time domain features with hidden Markov models

Interpretation of Spatial Relationships by Objects Tracking in a Complex Streaming Video

Review of Automatic Detection and Classification Techniques for Cetacean Vocalization

Contact Info

Product

Resources

About