Automatic Detectors for Underwater Soundscape Measurements

Environmental impact regulations require that marine industrial operators quantify their contribution to underwater noise scenes. Automation of such assessments becomes feasible with the successful categorisation of sounds into broader classes based on source types – biological, anthropogenic and physical. Previous approaches to passive acoustic monitoring have mostly been limited to a few specific sources of interest. In this study, source-independent signal detectors are developed and a framework is presented for the automatic categorisation of underwater sounds into the aforementioned classes

Review of Cetacean's click detection algorithms

The detection of echolocation clicks is key in understanding the intricate behaviors of cetaceans and monitoring their populations. Cetacean species relying on clicks for navigation, foraging and even communications are sperm whales (Physeter macrocephalus) and a variety of dolphin groups. Echolocation clicks are wideband signals of short duration that are often emitted in sequences of varying inter-click-intervals. While datasets and models for clicks exist, the detection and classification of clicks present a significant challenge, mostly due to the diversity of clicks' structures, overlapping signals from simultaneously emitting animals, and the abundance of noise transients from, for example, snapping shrimps and shipping cavitation noise. This paper provides a survey of the many detection and classification methodologies of clicks, ranging from 2002 to 2023. We divide the surveyed techniques into categories by their methodology. Specifically, feature analysis (e.g., phase, ICI and duration), frequency content, energy based detection, supervised and unsupervised machine learning, template matching and adaptive detection approaches. Also surveyed are open access platforms for click detections, and databases openly available for testing. Details of the method applied for each paper are given along with advantages and limitations, and for each category we analyze the remaining challenges. The paper also includes a performance comparison for several schemes over a shared database. Finally, we provide tables summarizing the existing detection schemes in terms of challenges address, methods, detection and classification tools applied, features used and applications.Comment: 23 pages, 6 tables, 4 figure

Detecting the presence of sperm whales echolocation clicks in noisy environments

Sperm whales (Physeter macrocephalus) navigate underwater with a series of impulsive, click-like sounds known as echolocation clicks. These clicks are characterized by a multipulse structure (MPS) that serves as a distinctive pattern. In this work, we use the stability of the MPS as a detection metric for recognizing and classifying the presence of clicks in noisy environments. To distinguish between noise transients and to handle simultaneous emissions from multiple sperm whales, our approach clusters a time series of MPS measures while removing potential clicks that do not fulfil the limits of inter-click interval, duration and spectrum. As a result, our approach can handle high noise transients and low signal-to-noise ratio. The performance of our detection approach is examined using three datasets: seven months of recordings from the Mediterranean Sea containing manually verified ambient noise; several days of manually labelled data collected from the Dominica Island containing approximately 40,000 clicks from multiple sperm whales; and a dataset from the Bahamas containing 1,203 labelled clicks from a single sperm whale. Comparing with the results of two benchmark detectors, a better trade-off between precision and recall is observed as well as a significant reduction in false detection rates, especially in noisy environments. To ensure reproducibility, we provide our database of labelled clicks along with our implementation code.Comment: 10 pages and 10 figure

The significance of passive acoustic array-configurations on sperm whale range estimation when using the hyperbolic algorithm

In cetacean monitoring for population estimation, behavioural studies or mitigation, traditional visual observations are being augmented by the use of Passive Acoustic Monitoring (PAM) techniques that use the creature’s vocalisations for localisation. The design of hydrophone configurations is evaluated for sperm whale (Physeter macrocephalus) range estimation to meet the requirements of the current mitigation regulations for a safety zone and behaviour research. This thesis uses the Time Difference of Arrival (TDOA) of cetacean vocalisations with a three-dimensional hyperbolic localisation algorithm. A MATLAB simulator has been developed to model array-configurations and to assess their performance in source range estimation for both homogeneous and non-homogeneous sound speed profiles (SSP). The non-homogeneous medium is modelled on a Bellhop ray trace model, using data collected from the Gulf of Mexico. The sperm whale clicks are chosen as an exemplar of a distinctive underwater sound. The simulator is tested with a separate synthetic source generator which produced a set of TDOAs from a known source location. The performance in source range estimation for Square, Trapezium, Triangular, Shifted-pair and Y-shape geometries is tested. The Y-shape geometry, with four elements and aperture-length of 120m, is the most accurate, giving an error of ±10m over slant ranges of 500m in a homogeneous medium, and 300m in a non-homogeneous medium. However, for towed array deployments, the Y-shape array is sensitive to angle-positioning-error when the geometry is seriously distorted. The Shifted-pair geometry overcomes these limits, performing an initial accuracy of ±30m when the vessel either moves in a straight line or turns to port or starboard. It constitutes a recommendable array-configuration for towed array deployments. The thesis demonstrates that the number of receivers, the array-geometry and the arrayaperture are important parameters to consider when designing and deploying a hydrophone array. It is shown that certain array-configurations can significantly improve the accuracy of source range estimation. Recommendations are made concerning preferred array-configurations for use with PAM systems

Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.Comment: 58 Pages, 5 Figure