## 1 Introduction
On the contrary, non-GAN framework approaches to learning the mapping function may be incapable of enhancing noisy speech with improvements in intelligibility and auditory perception as compared with a GAN-based T-F masking approach. Prior research works on GNN-based target speech enhancement are presented in [27; 28; 29; 30]. More information about graph signal processing concepts and applications of graph neural frameworks can be found in [37; 38; 39]. As the name indicates, the proposed deep neural architecture is actually a U-Net framework [40], which incorporates residual dense network (RDN) [41; 42; 43] modules in the encoder and decoder pathways. The residual block in the RDN module improves the generalization capability of the network architecture, while the dense convolutional module [44] within the RDN facilitates feature re-use, promotes gradient flow among the neural layers and reduces the number of parameters, leading to an overall improvement in accuracy and reduction in computational complexity. The presence of a GCRNN module in the bottleneck and skip-connection layers improves the feature extraction capability of the overall architectural framework. Due to the presence of the GCRNN module, the multi-channel latent space of the signals is transformed into a graph domain, resulting in an increase in the flexibility in exploitation of spatiotemporal and spectral information. The neural architecture for speech enhancement used in this work combines the advantageous features of both GANs and GCRNNs to provide good performance under noisy acoustic conditions. The overall GAN-based setting can help the architecture to undergo a discriminative process involving minimization of the distributional divergence between the model and data distribution, while the GCRNN embedded within the bottleneck layer can facilitate exploitation of the implicit information (i.e., spatial and spectro-temporal information) through transformation of the multi-channel latent space at the end of the encoder path to graph domain. Consequently, the overall speech intelligibility is improved under noisy acoustic conditions as compared with existing approaches. While the work presented in this article attempts to present an innovative speech enhancement methodology with the help of MS-GLMB filtering and neural architecture-assisted beamforming, it utilizes an acoustic parameter setup such as microphone array geometry and positions as well as room dimensions which have been already utilized in prior research works [6; 26; 31; 45]. While some of the simulation parameters might be similar, the approach presented in this article differs from the works in [6; 26; 31; 45] due to the utilization of MS-GLMB filtering to create the datasets (incorporating the effects of sources' motion and reverberation) that are used to train the neural architecture. The contributions of this paper are two-fold:  
1. Construction of label-set data for training a neural architecture for target speech enhancement with the help of MS-GLMB filtering-based multi-source tracking and a time-varying MVDR beamformer, one which takes into account the effect of reverberation, noise and motion of the sources at each time frame. While the GLMB-assisted tracking relies on initial location information provided by steered response power phase transform (SRP-PHAT) [46], the inclusion of the tracking framework within the neural training procedure helps to develop the knowledge of spectro-spatial information of the moving sources within the neural architecture, so that it is able to generalize to \"unseen\" acoustic conditions as well and directly predict the desired T-F mask. 2. Construction of a residual dense convolutional U-Net architecture with an embedded GCRNN module, referred to as RD-graph-U-Net, which is applied in a GAN setting to predict the target source-specific mask. The proposed approach is tested on publicly available datasets [47; 48; 49; 50; 51] using speech quality evaluation metrics [52; 53; 54].