## 5 Discussion  
The MS-GLMB filtering can solve the space-time permutation problem. Hence, its application in the source tracking-assisted beamforming procedure is useful to generate the labels (i.e., identities) of the multiple sources, along with their location estimates, across all of the time frames, with high accuracy even under low SNR and high reverberation and while sources change their positions and speak intermittently. The estimated labels are instrumental in correctly discriminating between the target speech source and interferers, which facilitate the construction of time-varying MVDR beamformers and subsequent T-F mask corresponding to the target source. This in turn can enhance the target speech in a dynamic manner within each time frame, even under the combined effect of imperfections caused by the sources' motion, reverberation and background noise. The T-F masks thus generated form an effective target label set for the proposed neural architecture, which is robust to imperfections caused by background noise, reverberation and motion of the sources. As an appropriate motion model such as the Langevin model is considered in modelling the motion of the sources while implementing the MS-GLMB filter, the dynamic changes in positions of the mobile sources are accounted for, which is reflected in the training procedure of the proposed residual dense Graph-U-Net-based neural speech enhancement model. In prior works concerning speech enhancement using deep neural architectures [8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30], the dynamics of the sources in motion using appropriate motion models were not considered, which is why the state-of-the-art deep neural architectures employed in prior research works may not provide satisfactory performance under the combined effect of imperfections caused by reverberation, noise and interfering sources. As evident in this work, the label generation procedure for training the neural architecture considers the sources' motion, whose trajectories possess different start and termination points as well as different velocities. Moreover, the circumvention of the space-time permutation problem by the MS-GLMB filtering leads to correct discrimination between the target source and the interfering sources across all of the time frames, which in turn assists the training label generation procedure to generate the T-F masks with high accuracy. The proposed neural framework effectively learns the mapping from the spectrograms corresponding to a received multichannel reverberant mixture of speech signals belonging to the multiple sources at different spatial positions to the T-F mask corresponding to the target source. Through this overall procedure, the knowledge about the sources' spatial information, as well as the dynamic beamformer's operation within each time frame, are implicitly included within the training procedure of the proposed neural framework, which enables the trained version to generalize to unseen conditions such as different RIRs, SNRs and variations in the sources' motion. While the research work outlined in [18; 19; 20], relying on \"complex spectral mapping,\" as well as the research works in [14; 15], aim to predict the target source-specific mask to enhance the target speech, as mentioned earlier they do not consider the motion of the sources, nor do they consider the random nature of the cardinality of the sources while training the respective neural architectures. Hence, compared with those approaches, the approach presented in this article performs better under dynamic conditions. As pointed out in [19; 25; 42; 43], the goal of dense connections is to maximize the information processing from all layers, whereas the inclusion of residual connections leads to an improved utilization of information as well as preservation of signal integrity. The combination of dense and residual connections leads to the formation of the residual dense block which improves the network efficiency.