Abstract: Considering the low signal-to-noise ratio (SNR) and Doppler frequency shift of the line spectrum of a moving sound source, this paper proposes a deep learning method based on supervised learning to achieve line spectrum enhancement. The training datasets are locally generated clean and noisy line spectra, which are trained by mapping and masking methods, respectively. Finally, the simulation data and the SWellEx-96 experimental data are compared and verified with the traditional enhancement method. For the simulation data, both models can be used for line spectrum enhancement, and both models could enhance multi-line spectrum and line spectrum with Doppler frequency shift. When the input SNR is −35 dB, the two models can achieve about 11.74 dB SNR gain and 7.52 dB noise reduction, respectively. For the SWellEx-96 dataset, the masking model has undistorted property for the line spectra with Doppler shift, which proves the feasibility of applying the proposed deep learning model to enhance the line spectra of moving sound sources.