## 4 Experimental Results
### Training Results of the Proposed Models With or Without Pruning (Techniique)
#### 4.1.2 Experimental Results of Deep Neural Network Model

Table 4: Overall signal performance comparison of proposed autoencoder models with conventional methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Encoder** & **Parameter (Encoder)** & **Parameter (RVQ)** & **MMACs (Encoder)** & **MMACs (RVQ)** \\ \hline Conventional [6] & 3.68 M & - & 36.82 & - \\ Proposed & 3.63 M & 5.12 M & 36.30 & 51.2 \\ Proposed–Pruned & 1.03 M & 5.12 M & 10.30 & 51.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational and parameter requirements for the model in sonobuoy systems. The proposed method demonstrated a 60.58% reduction in Spectrogram MSE for CW signals and a 55.22% reduction in Spectrogram MSE for LFM signals compared to the existing method. Simultaneously, improvements in LSD and SNR metrics were observed. The Proposed-Pruned model maintained a comparable level of quality to the proposed method without significant performance degradation, further proving its efficiency. Furthermore, the quantitative metrics for target signals, which are measured only in the target interval, are presented in Table 5, which supports the improvement tendency in Table 4.  
The spectrogram analysis in Figure 5. reveals no significant visual differences between the proposed method and its lightened version. However, when comparing the proposed method with the existing method [6], a substantial reduction in reconstruction errors in non-signal (or background noise only) regions was observed for both CW and LFM signals using the proposed method. Additionally, in the existing method (c), frequency spectrum distortions were evident near the target frequency band at 3.75 kHz, resulting from incomplete signal reconstruction. These distortions introduced unwanted frequency components, degrading the quality of the reconstructed signal. In contrast, the proposed method effectively suppressed such distortions, maintaining a signal shape similar to the input signal even in regions with target signals. Consequently, the proposed method provided high-quality reconstructions closer to the original signal. This superiority is further demonstrated in the frequency analysis results presented in Figure 6. For CW signals, the proposed methods showed frequency characteristics near the target band of 3.75 kHz that were more similar to the input signal than the existing method. For LFM signals, while the existing method exhibited better resemblance to the original signal in the 10-11 kHz frequency range, the proposed methods showed higher similarity across all other frequency bands. This indicates that the proposed methods effectively maintain signal accuracy over a broader frequency range. In addition, the results in Figure 7 show that the proposed and Proposed-Pruned methods significantly reduced reconstruction errors (Spectrogram MSE) compared to the existing method while demonstrating high transmission efficiency. Specifically, for CW signals, the proposed method reduced reconstruction errors by approximately 60.65% compared to the existing method, and the Proposed-Pruned method achieved a reduction of about 53.79%. For LFM signals, the proposed method recorded a reduction of approximately 55.25%, while the Proposed-Pruned method achieved a 49.65% reduction. These results clearly confirm that the proposed methods improved both reconstruction accuracy and transmission efficiency compared to the existing method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Signal Type** & **Spectrogram MSE** & **LSD (dB)** & **SNR (dB)** \\ \hline \multirow{2}{*}{Conventional [6]} & CW & 0.00418 & 1.26 & 25.94 \\  & LFM & 0.02153 & 1.24 & 17.74 \\ \multirow{2}{*}{Proposed} & CW & 0.00204 & 1.21 & 29.22 \\  & LFM & 0.00812 & 1.18 & 21.62 \\ \multirow{2}{*}{Proposed–Pruned} & CW & 0.00254 & 1.23 & 28.62 \\  & LFM & 0.00918 & 1.20 & 21.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of proposed autoencoder models measured only in target interval with conventional methods.