# Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation
## 4 Results
### Discussion and Analysis
The convolutional denoising model, which was trained using dataset A, is tested on both synthetic and real measured data to assess its performance in terms of generalization. This also serves to validate the hypothesis that, if simulated data closely resembles real-world conditions, it can be effectively used as input for model training. The results from test sets A1 and A2 reveal that when the signal-to-noise ratio (SNR) ranges between 10 dB and 15 dB, the model achieves an impressive SNR gain of 21.58 dB and a recall rate of 0.9965 in test set A1. These outcomes slightly outperform those obtained from the real measured data in test set A2, where the model achieves an SNR gain of 17.3 dB and a recall of 1. When the model, trained on dataset A, is directly tested on test sets B1 and B2, designed to evaluate its transferability, there is a slight reduction in both SNR gain and recall for both synthetic and real measured data. This suggests that while the model retains some degree of transferability to different ocean environments, its performance is somewhat reduced. This observed decrease is likely attributable to overfitting, as synthetic data typically contain fewer patterns compared to the more intricate and varied real measured data. A comparison between datasets A and B further confirms that the environment in dataset B is more complex, as evidenced by the slight decline in both SNR gain and recall. This difference aligns with the environmental conditions under which the data was collected. While the model demonstrates excellent generalization and transferability, the most effective approach of our method is to first collect ocean noise data from a well-defined marine environment. This data, combined with simulation algorithms, can then be used to create a large and diverse training dataset. Once trained, the model can be deployed in relevant scenarios. Therefore, It is highly recommended to deploy our denoising model in the same marine environment from which the ocean noise was collected, ensuring optimal performance. Moreover, additional validation across a broader range of conditions, including more extreme scenarios, is warranted to fully assess the model's robustness and adaptability. LOFAR and DEMON (Detection of Envelope Modulation on Noise) are crucial tools for analyzing, detecting, and recognizing targets. While denoising LOFAR spectrograms is an essential preliminary step, the ultimate goal is to detect and identify targets, and extract key attributes such as location, position, and the number of propeller blades. Moving forward, integrating advanced denoising techniques with detection, recognition, and other analytical methods will be a key focus of future research and development. Additionally, compared to directly reconstructing the denoised signal into the original signal, the proposed method can only be applied to LOFAR spectrogram analysis, which imposes limitations on its use. Future work should explore extending the method to more general signal reconstruction tasks beyond LOFAR spectrogram analysis.