# Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation
## 4 Results
### Model Transferability Validation  
In practical applications, it is not possible to collect data at all times and all places for model training. Therefore, to verify the transferability and generalization on different marine areas, the aforementioned trained convolutional denoising model is tested on sea trial data B1. The testing result is shown in Table 3.  
The metrics of the deep convolutional denoising model on test dataset B1 are similar to those on test dataset A1. Although there is a slightly decrease in signal-to-noise ratio gain, the overall change is not significant. This indicates that the deep convolutional denoising model has a good transferability and generalization on different marine areas.The denoising effects for different signal-to-noise ratio ranges are shown in Figure 5, with the left image representing the raw LOFAR spectrogram and the right image representing denoised LOFAR spectrogram. The red arrows indicate the center frequencies of the simulated data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**SNR Range**} & \multicolumn{2}{c}{**MSCU-Net**[20]} & \multicolumn{2}{c}{**DPRNN**[21]} & \multicolumn{2}{c}{**CDM (Ours)**} \\ \cline{2-7}  & **G** & **Recall** & **G** & **Recall** & **G** & **Recall** \\ \hline \(-10\) dB to \(-5\) dB & 0.8834 & 0.1853 & 3.9762 & 0.1676 & **10.7929** & **0.2294** \\ \(-5\) dB to 0 dB & 2.6540 & 0.6563 & 8.7896 & 0.6347 & 18.0306 & 0.6780 \\ \(0\) dB to 5 dB & 5.2922 & 0.8414 & 10.9292 & 0.8543 & 20.9799 & **0.9061** \\ \(5\) dB to 10 dB & 9.3417 & 0.8423 & 13.6969 & 0.8825 & 21.0083 & **0.9631** \\ \(10\) dB to 15 dB & 13.9782 & 0.8547 & 13.9269 & 0.8892 & 21.5888 & **0.9965** \\ \(15\) dB to 20 dB & 18.1433 & 0.8571 & 16.3986 & 0.8362 & 21.7486 & **0.9965** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of different denoising methods on test set A1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(SNR_{\text{in}}\) & \(SNR_{\text{out}}\) & **G** & \(N_{\text{correct}}\) & \(N_{\text{all}}\) & **Recall** \\ \hline
12.80 dB & 30.10 dB & 17.30 & 6 & 6 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance metrics on test set A2.  
To further verify the transferability and generalization of the convolutional denoising model on real measured data from different marine areas, the model is tested on test set B2. The result is shown in Table 4.  
For the real measured data test set B2, the average signal-to-noise ratio of the underwater acoustic signal is 11.15 dB, while the average signal-to-noise ratio after denoising is 26.33 dB, resulting in an average signal-to-noise ratio gain of 15.18 dB and a recall rate of 88.73%. The metrics of the real measured data are quite similar to those of the simulation  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\mathbf{SNR_{in}}\) & \(\mathbf{SNR_{out}}\) & \(\mathbf{G}\) & \(\mathbf{N_{correct}}\) & \(\mathbf{N_{all}}\) & **Recall** \\ \hline
11.15 dB & 26.33 dB & 15.18 & 189 & 213 & 0.8873 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model performance metrics on test set B2.  
Figure 5: Different signal-to-noise ratio ranges of raw LOFAR spectrogram and denoised LOFAR spectrogram.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{SNR_{range}}\) & \(\mathbf{G}\) & \(\mathbf{N_{correct}}\) & \(\mathbf{N_{all}}\) & **Recall** \\ \hline \(-10\) dB to \(-5\) dB & 9.55 & 106 & 300 & 0.3533 \\ \(-5\) dB to \(0\) dB & 14.68 & 244 & 323 & 0.7554 \\ \(0\) dB to \(5\) dB & 16.65 & 265 & 280 & 0.9464 \\ \(5\) dB to \(10\) dB & 17.71 & 276 & 278 & 0.9928 \\ \(10\) dB to \(15\) dB & 19.62 & 293 & 294 & 0.9966 \\ \(15\) dB to \(20\) dB & 21.16 & 249 & 249 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance metrics on test dataset B1.  
data, which not only demonstrates the transferability and generalization of the convolutional denoising model but also validates the practical application value of the proposed simulation-enhanced underwater acoustic signal LOFAR spectrogram denoising method.