Research on the line spectrum denoising detection based on multi-scale feature autoencoder

Abstract

Line spectrum detection is an important research direction in the field of underwater acoustic target detection. The relevant features of the target can be obtained by using the target line spectrum in the signal power spectrogram, but the radiated noise signal is susceptible to the influence of external noise, which degrades the quality of the target line spectrum and thus affects the complete extraction of the line spectrum. A denoising method is proposed in this paper based on deep learning network architecture and classical filter to extract the target line spectrum from the power spectrogram with strong background noise while maintaining the continuity of the line spectrum and improving the efficiency of line spectrum detection. The simulated signal power spectrogram is used to train the multi-scale feature autoencoder to remove different scale noise interference in the line spectrum part and then pass the morphological attribute filter to remove the background noise further and keep the line spectrum continuity. The experimental analysis of the measured fishing vessel noise signal and the simulated signal shows that the algorithm proposed in this paper can effectively remove the line spectrum noise interference, improve the line spectrum signal-to-noise ratio quality, and realize the effective detection of the line spectrum.