Data-based modeling of propeller tip-vortex cavitation noise for realistic acoustic ship signature

Propeller tip-vortex cavitation (TVC) noise significantly influences the acoustic signature of a navigating vessel; however, its detailed simulation has not been extensively developed thus far, owing to the complexity of TVC noise. This paper proposes an advanced data-based method for generating propeller TVC noise to enhance the realism of passive sonar simulators. Generative adversarial networks are applied to randomly create waveforms of TVC noise sources, and a probabilistic representation of TVC occurrences is adopted to continuously generate them depending on the ship speed. The proposed method is validated using the performance metrics of statistical measures and auditory features. The signals simulated by the proposed method effectively represent the time–frequency characteristics of the propeller TVC noise depending on the ship speed.