# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 4 Discussion  
The following discussion section is included to explore the effectiveness of different components in our DeCGAN model. Specific experiment settings are described below. We first examined the contribution of the TFC-SA and DeConv-FFN blocks. Case 1 represents our DeCGAN model with its default configurations. For Case 2, we replaced the deformable convolution in the DeConv-FFN with dilated convolution; for Case 3, we completely removed all DeConv-FFN modules; finally, Case 4 introduced a triple-path self-attention (Triple-path SA) mechanism to displace TFC-SA, which extended the dual-path Transformer structure by incorporating an additional Transformer path to process the channel dimension. Next, the denoising performance of the two decoders was examined. In Case 4, only the Mask Decoder was retained; in Case 5, only the Complex Decoder was retained. Finally, in Case 6, we removed the metric discriminator to observe its impact on the model's speech enhancement performance. The experimental settings and results are presented in Tables 2 and 3. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Case** & **Dilated** & **Deformable** & **Tri-Path** & **TFC-SA** & **No. of** & **Mask** & **Complex** & **Discriminator** \\
**Index** & **Conv** & **Conv** & **SA** & **Blocks** & **Decoder** & **Decoder** & **Discriminator** \\ \hline
1 & & \(\surd\) & & \(\surd\) & 5 & \(\surd\) & \(\surd\) & \(\surd\) \\
2 & \(\surd\) & & & \(\surd\) & 5 & \(\surd\) & \(\surd\) & \(\surd\) \\
3 & & & & \(\surd\) & 5 & \(\surd\) & \(\surd\) & \(\surd\) \\
6 & & \(\surd\) & & \(\surd\) & 5 & \(\surd\) & \(\surd\) & \\
7 & & \(\surd\) & & \(\surd\) & 4 & \(\surd\) & \(\surd\) & \(\surd\) \\
8 & & \(\surd\) & & \(\surd\) & 6 & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The experimental settings of the ablation studies. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case Index** & **PESQ** & **CSIG** & **CBAK** & **COVL** & **STOI** \\ \hline
1 & 3.31 & **4.61** & **3.86** & **4.12** & **0.96** \\
2 & 3.18 & 4.52 & 3.76 & 3.98 & 0.95 \\
3 & 2.94 & 3.98 & 3.05 & 3.54 & 0.94 \\
4 & 3.26 & 4.6 & 3.79 & 4.08 & 0.96 \\
5 & 3.23 & 4.42 & 3.72 & 3.96 & 0  
A comparison between Cases 1, 2, and 3 verified the effectiveness of the DeConv-FNN module, indicating that in speech enhancement tasks, deformable convolution is more effective than dilated convolution in extracting local speech features by capturing the feature dependencies of each point within a local region. Next, by comparing Cases 1 and 4, we found that Case 4 showed slightly lower performance than Case 2, which demonstrated the effectiveness of TFC-SA. However, when the DeConv-FNN module was removed, performance significantly decreased; in this case, the model relied solely on TFC-SA. Conversely, when TFC-SA was replaced with the triple-path SA, the performance approached that of the model with the DeConv-FNN module intact. These results revealed the effective role of DeConformer in balancing the functions of the Transformer in extracting global features and of the CNN in extracting local features. By analyzing Cases 1, 7, and 8, we found that the performance barely showed any improvement when the number of DeConformer modules increased. Based on this analysis, the final model employs five DeConformer modules, achieving a balance between computational efficiency and feature extraction accuracy. By comparing Case 5 and Case 6, we could evaluate the denoising performance of the two decoders. The mask decoder utilizes only the magnitude spectrum as input; the denoised frequency spectrum was obtained by combining the reconstructed magnitude spectrum with the original phase. In contrast, in the complex decoder, the complex spectrum was used as the input to output the reconstructed complex spectrum.