# Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation
## 4 Results  
### Comparison with Baselines  
In this section, we evaluate the performance of our proposed convolutional denoising model(CDM) with two deep learning models: MSCU-net (multi-scale convolution U-Net) [20] and DPRNN (Dual-Path Recurrent Neural Network) [21]. These three models are trained on dataset A and tested on test set A1. However, MSCU-net is a two-dimensional model which treats the spectrogram of a noisy acoustic signal as input and the spectrogram of the clean acoustic signal as output. Thus, instead of using the soft threshold mask label for the target defined in Equation (9), the spectrogram of the clean acoustic signal is used as the label for model training, which is consistent with the training method described in the original paper. Additionally, DPRNN is a waveform-based model that directly denoises raw noisy acoustic signals, transforming them into clean signals, so the model directly uses the noisy one-dimensional acoustic signal as input and the clean one-dimensional acoustic signal as output for model training. The overall results are shown in Table 1.  
For the simulation test set A1, the training and testing data come from the same marine area, the convolutional denoising model achieves a signal-to-noise ratio (SNR) gain of over 20 dB for underwater target signal with an SNR above 0 dB, and the recall rate of the line spectrum is greater than 90%. In extreme low SNR conditions, specifically between \(-10\) dB and \(-5\) dB, where the ocean noise power is significantly higher than target signal power, the convolutional denoising model still provides an SNR gain of 10.7929 dB. Although the recall rate of the line spectrum is only 22.94%. Under these extremely low SNR conditions, the convolutional denoising model is still able to extract some line spectrum that is completely masked in the original LOFAR spectrogram. Compared to MSCU-Net and DPRNN, our proposed convolutional denoising model outperforms both in terms of SNR gains and recall across all SNR levels, demonstrating significantly superior denoising performance. These results demonstrate that the model effectively suppresses noise while preserving the essential features of the original target signal to the greatest extent. One possible reason for the performance difference between the models is the variation in their training objectives. The convolutional denoising model focuses on separating the noisy signal into distinct noise and target components, while the other two models aim to directly transform the noisy signal into a clean one. This difference in approach may introduce unintended artifacts or overfitting, which could negatively impact their denoising effectiveness.  
Furthermore, to verify the denoising effectiveness of the convolutional denoising model on real measurement data from the same marine area, using the same model as described above, tests are conducted on test dataset A2. The testing result is shown in Table 2.  
The results indicates effective noise reduction with gain \(G\) being 17.30, showing a substantial enhancement in signal quality. The recall of 1 demonstrates perfect identification of the target line spectrum in this dataset.