## 5 Results and Discussion
### Ablation Study  
To investigate the contribution of different components in the TS-CGANet that improved its performance, we conducted an ablation study and compared several variants  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **PESQ** & **CSIG** & **CBAK** & **COVL** & **STOI** \\ \hline Noisy & 1.97 & 3.35 & 2.44 & 2.63 & 0.91 \\ GCRN [35] & 2.71 & 4.12 & 3.23 & 3.41 & 0.94 \\ RNNoise [11] & 2.34 & 3.40 & 2.51 & 2.84 & 0.92 \\ PercepNet [12] & 2.73 & - * & - & - & - \\ CTS-Net [36] & 2.92 & 4.22 & 3.43 & 3.62 & 0.94 \\ DeepFilterNet [37] & 2.81 & - & - & - & - \\ S-DCCRN [38] & 2.84 & 4.03 & 2.97 & 3.43 & 0.94 \\ DMF-Net [18] & 2.97 & 4.26 & 3.25 & 3.48 & 0.94 \\ SF-Net [19] & 3.02 & **4.36** & 3.54 & 3.67 & 0.94 \\
**TS-CGANet** & **3.30** & 4.31 & **3.69** & **3.82** & **0.95** \\ \hline \hline \end{tabular}  
* *--/ denotes that the result was not provided in the original paper.  
\end{table}
Table 2: Performance comparison of Voice Bank + DEMAND dataset.  
of the TS-CGANet model in Table 3. These variants included: (i) LB-Net (full)--a single-stage complex and real dual-path full-band SE method that directly enhances full-band speech using LB-Net; (ii) LB-Net (with real CGAU)--a single-stage real dual-path full-band SE method that changes the complex CGAU in (i) to real CGAU; (iii) LB-Net (w/o phase enhancement)--a single-stage single-path full-band SE method that removes the complex CGAU structure and decoupling-style phase-aware method in the LB-Net and only uses the ME-Net to directly enhance full-band speech; (iv) TS-CGANet (w/o complex CGAU)--a two-stage real dual-path full-band SE method that changes the complex CGAU in TS-CGANet to real CGAU; and (v) TS-CGANet (w/o phase enhancement)--a two-stage single-path full-band SE method that removes the complex CGAU structure and decoupling-style phase enhancement method in the TS-CGANet.  
We conducted an ablation study to investigate the contribution of different components of the TS-CGANet to enhancing performance. All variants were set using the same configuration as the TS-CGANet. As indicated in Table 3, all variants underperformed compared to the TS-CGANet. Specifically, variant (i) exhibited a decrease of 0.14 in the PESQ. This may be attributed to the model's tendency to focus on the high-frequency band while processing high- and low-frequency band speech in parallel, which leads to a reduced effect on the low-frequency band containing the main speech components.  
When comparing variant (i) with variants (ii) and (iii), we found that all indicators further declined. Replacing the complex CGAU branch in the LB-Net with a real CGAU resulted in a lack of information interaction between the real and imaginary parts, which decreased the speech enhancement effect. Furthermore, removing the decoupling phase enhancement method resulted in a decrease of 0.35 in the PESQ, which significantly impacted the quality of the enhanced speech. These findings further demonstrate the effectiveness of our proposed complex and real dual-path network LB-Net.  
When comparing variants (ii) and (iii) with variants (iv) and (v), we observed that the speech enhancement effect of the two-stage network was superior to that of the single-stage network. This may be attributed to reasonable spectrum splitting, which enabled different networks to process the LB and HB components of speech separately, thereby avoiding overlapping and information loss between sub-bands and improving the effect and quality of the speech enhancement. These results provide additional support for the rationality of our proposed two-stage full-band speech enhancement method.