Hybrid Log-Mel and HPSS-Aided Convolutional Neural Network for Underwater Very-Low-Frequency Remote Passive Sonar Detection

Very-low-frequency (VLF) passive sonar detection is one of the core technologies for maritime surveillance, although its performance is often severely affected by strong impulsive ocean ambient noise interference. This paper, for the first time, proposes a convolutional neural network (CNN) detection framework with hybrid Log-Mel spectrogram (Log-Mel) and Harmonic–Percussive Source Separation (HPSS) preprocessing. Aiming to highlight the detailed features of low frequencies in accordance with impulsive noise interference removal, the network was trained on a measured dataset in the South China Sea for a whole week by maximize the area under receiver operating characteristic curve (AUC) that corresponds to a false alarm probability of less than 0.1. The test results show that compared with a typical Short-Time Fourier Transform (STFT) input feature, the utilization of Log-Mel and HPSS can be superior, especially utilizing Log-Mel and HPSS(H) features at the same time. Validation with a set of measured moving ship data shows that the detection performance of the proposed hybrid Log-Mel and HPSS-aided CNN can be stable and significantly improve the remote passive sonar detection performance.