# Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation
## 4 Results
### Discussion and Analysis  
This paper introduces an enhanced simulation algorithm designed to simulate target line spectrum features while integrating ocean noise. The primary objective of this algorithm is to generate a large volume of labeled data for model training. While previous studies have simulated data by mixing clean signals with noise, the performance of models can degrade where target data collection is challenging. To address this, we propose an enhanced simulation algorithm based on the assumptions that the pattern of line spectrum, such as the presence of Doppler effects, are known and if the simulated data closely resemble real data, they can be treated as realistic for model training purposes. A key feature of proposed enhanced simulation algorithm is the incorporation of the Doppler effect, which is particularly relevant for fast-moving vessels such as speedboats and unmanned underwater vehicles (UUVs). When direct data collection from a target is difficult and pattern of line spectrum is known, this algorithm can generate sufficient data for model training. Moreover, the pattern of the line spectrum does not necessarily indicate the type of target that must be known prior to model training. For instance, even for the same target, the inclusion or exclusion of the Doppler effect results in different line spectrum patterns. The parameters in the enhanced simulation are crucial for ensuring the quality of the training samples. The signal duration \(T\) controls the length of the LOFAR spectrogram, with an empirical value set to 40, which has minimal impact on the quality of the simulated data. The lower frequency limit \(f_{l}\) and upper frequency limit \(f_{h}\) define the frequency range where the line spectrum appears. When mixed with ocean noise, it is important to ensure that the target frequency range remains clean, meaning that no unexpected line spectrum are present, as this could degrade the denoising model's performance. The lower amplitude limit \(A_{l}\) and upper amplitude limit \(A_{h}\) control the prominence of the line spectrum. Higher values make the line spectrum more noticeable. Since it is difficult to denoise noisy data where the line spectrum is submerged in ocean noise, we aim for slightly weaker amplitude samples during the simulation, rather than higher amplitude samples, to better reflect complex conditions. The number of lines spectrum \(N\) varies across samples and depends on the frequency range of the spectrogram. A wider frequency range requires more line spectrum, as the ratio of line spectrum pixels to background pixels decreases, which introduces a significant pixel imbalance problem that can negatively affect the model's denoising performance. The amplitude of frequency drift \(f_{d}\) and amplitude drift \(A_{d}\) introduce variability in the training samples and are tailored to the pattern of the line spectrum. For large vessels, such as cargo ships, the line spectrum is typically found in the low-frequency range, with minimal Doppler shifts due to their slower speeds. In contrast, for unmanned underwater vehicles (UUVs), significant frequency drift \(f_{d}\) and amplitude drift \(A_{d}\) must be applied to account for their higher speeds. If the underwater acoustic signal contains a rapidly moving vessel but the parameters \(f_{d}\) and \(A_{d}\) are not properly considered in the simulation, the denoising effect will deteriorate. In this paper, two datasets, A and B, are introduced to evaluate the model's generalization ability and transferability across different oceanic environments, where both simulation data and sea trail data are included. The ocean noise in these datasets is sourced from distinct locations, with synthetic data present in test sets A1 and B1, and real measured data in test sets A2 and B2. Compared to the ocean noise in dataset A, the noise in dataset B is more complex, featuring multiple vessels within the surrounding environment.