## 4 Computer Simulation Results
### Evaluation Metrics
#### 4.4.4 Ablation Study  
In order to investigate the contribution of different components in the proposed neural speech enhancement architecture, in this sub-section we conduct an ablation study. In this study, we compare variants of the proposed speech enhancement approach whereby the results are presented in Table 11.  
In Table 11, the PESQ and ESTOI values corresponding to variants of the proposed approach are listed, and these clearly demonstrate the superior performance achieved by the proposed approach. The entry 'w/o GCRNN block in skip connections' indicates that the GCRNN blocks were removed from the skip connections of the overall U-Net architecture, while including the GCRNN in the bottleneck layer of the U-Net. There is a decrease in PESQ value by 0.05 for the reverberation time RT60 of 550 ms after the removal of the GCRNN blocks from the skip connections, which is due to reduction in feature extraction capability of the neural architecture. As the GCRNN block in the bottleneck layer is still left unchanged, some degree of flexibility in the exploitation of spatiotemporal and spectral information is still maintained. There is also a slight reduction in ESTOI  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Speech Enhancement Approach** & \multicolumn{2}{c}{**PESQ**} & \multicolumn{2}{c}{**ESTOI**} \\ \hline  & \(p\)**-Value** & **F-Value** & \(p\)**-Value** & **F-Value** \\ \hline Proposed approach -\(>\) noisy speech & 0.001 & 50.25 & 0.001 & 54.24 \\ \hline Proposed approach -\(>\) MS-GLMB filtering-assisted MVDR beamformer & 0.023 & 12.17 & 0.042 & 09.45 \\ \hline Proposed approach -\(>\) mask MVDR with self-attention & 0.021 & 20.07 & 0.031 & 15.15 \\ \hline Proposed approach -\(>\) U-Net with embedded GCN & 0.012 & 14.76 & 0.039 & 12.15 \\ \hline \end{tabular}
\end{table}
Table 10: ANOVA analysis of speech enhancement methods at 95% confidence interval.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Method** & \multicolumn{2}{c}{**RT60 = 550 ms**} & \multicolumn{2}{c}{**RT60 = 250 ms**} & \multicolumn{2}{c}{**RT60 = 50 ms**} \\ \hline  & **PESQ** & **ESTOI** & **PESQ** & **ESTOI** & **PESQ** & **ESTOI** \\ \hline Proposed approach & 2.70 & 75.00\% & 2.88 & 79.60\% & 2.90 & 80.50\% \\ \hline w/o GCRNN block in skip connections & 2.65 & 72.60\% & 2.84 & 76.70\% & 2.87 & 78.40\% \\ \hline w/o Residual block in encoder and decoder & 2.57 & 69.50\% & 2.75 & 72.22\% & 2.78 & 76.32\% \\ \hline w/o Discriminator & 2.67 & 74.43\% & 2.85 & 77.45\% & 2.89 & 79.20\% \\ \hline \end{tabular}
\end{table}
Table 11: Results of ablation study.  
value due to removal of the GCRNN blocks from the skip connections, which indicates a slight reduction in speech intelligibility. The entry 'w/o Residual block in Encoder and Decoder' in Table 11 indicates the removal of residual blocks from the encoder and decoder pathways of the U-Net architecture while keeping other components intact. It is evident that the removal of residual blocks leads to loss of feature preservation in the overall neural architecture, which is why the PESQ and ESTOI values become diminished. The entry 'w/o Discriminator' refers to the case in which the discriminator of the GAN framework is removed. As expected, the PESQ and ESTOI values are reduced as compared with the original proposed approach, as the non-GAN neural framework is incapable of discriminating between the original desired signal and the noisy received signal at the input and is also prone to adversarial conditions.