Research on Ship Radiated Noise Separation Method Based on Neural Network Autoencoder Combined With Time-Frequency Masking - Xia, Zhi; Zhang, Ziang; Shi, Jie; Zhao, Zihao (IEEE journal of oceanic engineering, 2025)
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.