## 1 Introduction
An interesting research work in this direction can be found in [33], where it has been demonstrated through computer simulation results that the utilization of the outputs of the GLMB-based multi-target tracking filter by the subsequent deep reinforcement learning (RL) module leads to better tracking performance. The overall system proposed in [33] utilizes the observations from the GLMB filter to learn the system dynamics and the reward function and ultimately leads to the prediction of each target's next states. However, the domain of application of the methodology presented in [33] is different from acoustic domain, and the application area is different from speech enhancement. In this article, we have utilized the MS-GLMB filter to assist in the construction of training label sets for the subsequent neural target speech enhancement module. The training procedure considers the relative motion of the different acoustic sources within the indoor environment, as well as the effects of reverberation and noise, which leads to improved training of the neural architecture. As such, the proposed system is capable of dynamically enhancing the target speech in the cocktail party scenario. In some previous works, an angular/directional feature was used to assist neural speech enhancement [13; 15; 16; 17; 18; 20] by using visual information, and was utilized to construct a directional feature which can lead to robust performance. However, in situations where visual information suffers from impairments, such as low light ambient conditions, camera jitters, or obstruction in the field of view (FoV) of the camera due to motion of the sources, the acoustic information, such as the speech signal, is the only source of information which needs to be exploited by the neural beamformer to perform target speech enhancement. In the approach presented in this article, the impact of reverberation and the sources' motion at each time frame are considered by the MS-GLMB filtering to provide estimates of the sources' positions at each time frame as well as their associated labels, which in turn help to train the subsequent neural architecture to predict the desired mask corresponding to the target speech source even in the presence of reverberation, noise and the motion of the sources. In other words, the MS-GLMB filtering assists the neural architecture to discriminate the target source from the interfering sources and to learn the mapping from the input features to the target source-specific mask with higher accuracy, leading to improved target speech enhancement performance. The neural architecture used in this work to model the beamformer operation is a residual dense convolutional U-Net framework with a graph convolutional recurrent neural network (GCRNN) [34] module embedded within the bottleneck and the skip connection layers. The neural framework can be referred to as a residual dense graph-U-Net (RD-graph-U-Net) applied in a generative adversarial network (GAN) [35; 36] setting. Details about the neural architecture are presented in Section 3. While traditional supervised techniques based on deep convolutional neural frameworks have demonstrated good speech enhancement performance, they are prone to unseen adversarial conditions, which can render the overall neural framework incapable of discriminating between the original desired signal and noisy received signal at the input. The mask estimation framework based on GAN [21; 22; 23; 24; 25] is an alternative to the conventional CNN-based framework, whereby the GAN learns a mapping function from the noisy received signal to the time-frequency (T-F) mask by undergoing a discriminative process involving minimization of the distributional divergence between the model and data distribution. A GAN-based T-F mask estimation framework can lead to considerable improvement in speech intelligibility under noisy acoustic conditions while being flexible in application to real-time implementations and also generalizable to different T-F representations.