Optimization of underwater acoustic detection of marine mammals and ships using CNN

Due to the intensification of the exploitation of the oceans, it becomes crucial to better and at a larger scale monitor marine life before impacting operations and to monitor the evolution of the ambient noise in known habitats. This is usually done with hydrophones deployed underwater at the vicinity of the site of interest. However, the processing of the collected data is a laborious task where an expert must listen to and look at each time-frame to ensure the quality of the results. The automation of the sound identification has boomed since the progress of machine learning and in particular computer vision. Indeed, models based on Convolutional Neural Network (CNN) can now identify marine mammals or other ambient sounds in a record time. However, the question of the generalization to multiple sites of such models is barely studied. This paper trains a simple CNN with the ShipEars and DOSITS datasets to identify large vessels, small vessels, dolphins, and background noise. The model is then validated with a sample of the REP(MUS) dataset. Three types of optimizations are proposed to improve the model’s performance on REP(MUS): the data variability, the time discretization, and a Bayesian optimization of the hyperparameters.