## 4 Computer Simulation Results
### Evaluation Metrics
#### 4.4.2 Performance Comparison of Proposed Approach on Simulated and Recorded RIRs  
From Table 7, it is evident that the proposed approach of the residual dense Graph-U-Net, assisted by MS-GLMB filtering, outperforms the other state-of-the-art approaches of target speech enhancement under reverberant conditions, when the sources are in motion with intermittent speech activity. This is because the spectro-spatial features extracted by the MS-GLMB filtering framework provides training data for the proposed neural architecture by incorporating the effects of reverberation and motion of the sources, which helps the proposed neural architecture to learn the mapping from input spectro-spatial features to the target source-specific mask better than other approaches where a motion model of the speech sources is not considered. In Table 8, ESTOI, PESQ, SI-SNR and SDR are used as the evaluation metrics of choice. Compared with STOI, ESTOI provides a more comprehensive assessment of the intelligibility of enhanced speech by considering additional factors such as additive and convolutional distortions that are commonly encountered in real-world scenarios. As observed from Table 8, the TI_MVDR cannot handle the moving sources well because the estimated SCMs by TI_MVDR are time-invariant in nature, which causes the beamforming filter coefficients to also be time invariant. The online_MVDR method does appear to track moving sources but, in this approach, the tracking seems to depend on the forgetting factor that is responsible for providing exponentially less weight to the older ISCMs. Therefore, it is cumbersome to tune this parameter in order to offer optimal performance for various acoustic conditions that may arise during movements of the speech sources. The BLK_MVDR approach also suffers from sub-optimal performance due to the challenge involved in tuning the block sizes during online SCM computation. Because, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Method**} & \multicolumn{3}{c}{**Office Room**} & \multicolumn{3}{c}{**Corridor**} \\ \multicolumn{1}{c}{**(RT60 = 0.52 s)**} & \multicolumn{3}{c}{**(RT60 = 1.25 s)**} \\ \hline  & \begin{tabular}{c} PESQ \\ \end{tabular} & \begin{tabular}{c} ESTOI (\%)** \\ \end{tabular} & \
\begin{tabular}{c} PESQ \\ \end{tabular} &
\begin{tabular}{c} ESTOI (\%)** \\ \end{tabular} \\ \hline RD-Graph-U-Net (proposed approach) & \(3.06\) & \(77.40\) & \(2.56\) & \(75.58\) \\ \hline MS-GLMB filter-assisted MVDR & \(2.77\) & \(74.46\) & \(2.25\) & \(72.23\) \\ \hline Mask-MVDR with self-attention & \(2.85\) & \(75.56\) & \(2.36\) & \(73.39\) \\ \hline U-Net with embedded GCN & \(2.84\) & \(75.32\) & \(2.29\) & \(72.78\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation of proposed approach on recorded RIRs. during online operation, the objective is to perform beamforming based on an adequate number of signal samples received within a short time interval, it is challenging to tune the block size of received signals so as to provide optimal performance with this approach. The DOA_MVDR baseline approach serves as a benchmark for evaluating the performance of other beamforming approaches whereby an 'oracle' approach is designed with a priori knowledge of the DOAs. While it helps in performance evaluation, in practical scenarios, perfect knowledge of the DOAs cannot be obtained a priori because the estimated DOA information will be contaminated by noise imperfections. Finally, the mask-based MVDR beamformer approach with self-attention neural architecture seems to provide good performance under source movements due to the capability of the self-attention-based neural architecture to focus on a particular DOA direction. However, all of these beamforming approaches suffer from the assumption of a fixed cardinality of the sources, whereas the cardinality of the speech sources may vary due to the appearance and disappearances of the sources at different time frames within the acoustic environment.