## 4 Computer Simulation Results
### Evaluation Metrics  
The proposed methodology is compared with other methodologies which have also been applied to the moving speech sources scenario. As evaluation metrics, enhanced short-time objective intelligibility (ESTOI) [51], perceptual evaluation of speech quality (PESQ) [52], SI-SNR [18] and signal-to-distortion ratio (SDR) [53] are used for performance comparison in this work. In this section, the spectrograms of the clean target speech, reverberant mixture and enhanced speech are first presented for comparison. Second, the proposed approach is evaluated against other state-of-the-art neural speech enhancement approaches in terms of some established evaluation metrics. Figure 7a-c depict the spectrograms of clean target, reverberant mixture, and enhanced speech corresponding to waveforms selected from the LibriSpeech dataset.  
Figure 7: (**a**) Spectrogram of the clean speech signal belonging to the target source, (**b**) spectrogram of the reverberant mixture of speech signals corresponding to 3 sources, (**c**) spectrogram of the enhanced speech signal corresponding to target source. The colored portion of the spectrograms between 0.5 s and 1.3 s denote the presence of speech (_All speech signals belong to LibriSpeech dataset_).  
Figure 8a-c depict the spectrograms of the clean target, reverberant mixture, and enhanced speech corresponding to the waveforms selected from the Ws2J0 mix dataset.  
As evident from the spectrograms presented in Figures 7 and 8, the proposed neural approach of speech enhancement successfully cancels the background noise and reverberation to enhance the target speech, even when the sources are in motion and intermittently speaking. This is evident from Figures 7c and 8c. Next, Table 7 shows a performance comparison in terms of objective metrics such as PESQ and SI-SNR between the proposed approach and other state-of-the-art approaches at three different RT60 values (550 ms, 250 ms and 50 ms), which tackle the problem of speech enhancement including speech dereverberation and interference cancellation under dynamic scenarios.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**RT60 = 550 ms**} & \multicolumn{2}{c}{**RT60 = 250 ms**} & \multicolumn{2}{c}{**RT60 = 50 ms**} \\ \hline  & **PESQ** & **SI-SNR (dB)** & **PESQ** & **SI-SNR (dB)** & **PESQ** & **SI-SNR (dB)** \\ \hline MS-GLMB filter-assisted DS & 2.41 & 4.42 & 2.79 & 10.73 & 2.91 & 13.94 \\ TI-MVDR & 2.43 & 2.35 & 2.73 & 4.42 & 2.76 & 5.13 \\ Online-MVDR & 3.22 & 4.85 & 3.38 & 5.62 & 3.45 & 6.52 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of different state-of-the-art approaches for speech dereverberation under dynamic conditions at 3 different reverberation times.  
Figure 8: (**a**) Spectrogram of the clean speech signal belonging to the target source, (**b**) spectrogram of the reverberant mixture of speech signals corresponding to 3 sources, (**c**) spectrogram of enhanced speech signal corresponding to the target source. The colored portion of the spectrograms between 0.5 s and 1.3 s denote the presence of speech (_All speech signals belong to WS2J0 dataset_).