Gas bubbles in the ocean are produced by breaking waves, rainfall, methane seeps, exsolution, and a range of biological processes including decomposition, photosynthesis, respiration and digestion. However one biological process that produces particularly dense clouds of large bubbles, is bubble netting. This is practiced by several species of cetacean. Given their propensity to use acoustics, and the powerful acoustical attenuation and scattering that bubbles can cause, the relationship between sound and bub-ble nets is intriguing. It has been postulated that humpback whales produce ‘walls of sound’ at audio frequencies in their bubble nets, trapping prey. Dolphins, on the other hand, use high frequency acous-tics for echolocation. This begs the question of whether, in producing bubble nets, they are generating echolocation clutter that potentially helps prey avoid detection (as their bubble nets would do with man-made sonar), or whether they have developed sonar techniques to detect prey within such bubble nets and distinguish it from clutter. Possible sonar schemes that could detect targets in bubble clouds are proposed, and shown to work both in the laboratory and at sea. Following this, similar radar schemes are proposed for the detection of buried explosives and catastrophe victims, and successful laboratory tests are undertaken.

In order to overcome the shortcomings of the dolphin algorithm, which is prone to falling into local optimum and premature convergence, an improved dolphin swarm algorithm, based on the standard dolphin algorithm, was proposed. As a measure of uncertainty, information entropy was used to measure the search stage in the dolphin swarm algorithm. Adaptive step size parameters and dynamic balance factors were introduced to correlate the search step size with the number of iterations and fitness, and to perform adaptive adjustment of the algorithm. Simulation experiments show that, comparing with the basic algorithm and other algorithms, the improved dolphin swarm algorithm is feasible and effective.

In situ time series measurements of ocean ambient noise, have been made in deep waters of the Arabian Sea, using an autonomous passive acoustic monitoring system deployed as part of the Ocean Moored buoy network in the Northern Indian Ocean (OMNI) buoy mooring operated by the National Institute of Ocean Technology (NIOT), in Chennai during November 2018 to November 2019. The analysis of ambient noise records during the spring (April–June) showed the presence of dolphin whistles but contaminated by unwanted impulsive shackle noise. The frequency contours of the dolphin whistles occur in narrow band in the range 4–16 kHz. However, the unwanted impulsive shackle noise occurs in broad band with the noise level higher by ∼20 dB over the dolphin signals, and it reduces the quality of dolphin whistles. A wavelet based threshold denoising technique followed by a subtraction method is implemented. Reduction of unwanted shackle noise is effectively done and different dolphin whistle types are identified. This wavelet denoising approach is demonstrated for extraction of dolphin whistles in the presence of challenging impulsive shackle noise. Furthermore, this study should be useful for identifying other cetacean species when the signal of interest is interrupted by unwanted mechanical noise.