Stochastic Ship-radiated Noise Modelling via Generative Adversarial Networks

The design and performance evaluation of underwater acoustic (UA) communication systems in shallow water and harbour environments is a continuous challenge due to the numerous degrading factors present in the UA channel, one of which is the presence of noise generated due to nearby shipping activity. However, few research studies have examined the properties of ship-radiated noise in terms of its time-domain statistical characteristics and its negative effects on UA communication systems. We propose the use of unsupervised learning techniques to train generative models that capture the time-domain stochastic behaviours of ship-radiated noise using a publicly available database of long-term acoustic shipping noise recordings. These models can then be used for further analysis of ship-radiated noise and performance evaluation of UA orthogonal frequency-division multiplexing systems in the presence of such interference. For further validation, we include experimentally acquired ship-radiated noise recordings acquired off the coast of Caesarea, Israel. The results indicate a two component Gaussian mixture model serves as a better approximation for high frequency ship-radiated noise while generative adversarial networks produce improved realizations of shipping noise in lower frequencies.