Research on Ship Radiated Noise Separation Method Based on Neural Network Autoencoder Combined With Time–Frequency Masking

Strong narrowband signals mixed with weak broadband signals can interfere with the direction estimation of broadband targets using the cross-correlation method of a two-hydrophone array. To tackle this problem, this article proposes a method for separating narrowband signals from sonar-received signals. The proposed method integrates neural network autoencoders with time-frequency masking techniques. We have designed a lightweight neural network autoencoder that can be trained using purely simulated data. This autoencoder extracts narrowband line spectrum features from the time–frequency distribution of the sonar-received signals and generates time–frequency masks. Subsequently, the time-frequency masking method is employed to isolate the narrowband components from the sonar-received signals. The proposed method was validated using data from the SWellEx-96 experiment. In the cross-correlation results of the original data from two hydrophones, strong narrowband signals dominated and obscured the weaker broadband signals. By removing the narrowband components from the two-hydrophone signals using the method proposed in this article, the cross-correlation of the processed data clearly revealed the time history of the broadband signal characteristics. This result confirms the effectiveness of the proposed method.