Abstract
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.
Highlights
The increasing human anthropogenic activities have significantly changed the soundscape in oceans
The unique labels were used to classify the detected sound sources. This new approach is a modern way to carry out unsupervised detection and classification in the time-domain depending entirely on Empirical mode decomposition (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.in [45], the generated intrinsic mode function (IMF) 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
Ecosystem managers are concerned about mitigating these threats but have challenges of inadequate information about them
Summary
The increasing human anthropogenic activities have significantly changed the soundscape in oceans. Usman et al.: Review of Automatic Detection and Classification Techniques for Cetacean Vocalization conservation and protection [6], [13] They are faced with the challenge of inadequate knowledge on the ecosystems of these mammals [1], [13]. The large acoustic data collected during the recordings are difficult to analyze by the human operators, the need to have an algorithm that can automatically detect and classify these large volume of recorded sounds. This helps in processing the large acoustic datasets relatively faster and with consistency.
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