## 2 System Model
### Spectro-Spatial Information-Guided T-F Domain Masking for GSC-MVDR Beamformer  
In prior state-of-the-art research works on neural network-assisted target speech enhancement [11; 12; 13], the angular feature (AF) was computed and used to construct an input feature set for the subsequent DL architecture, which predicts the target label (e.g., a mask specific to the target source). While the works outlined in [11; 12; 13] use AF as input feature for transformer- and recurrent neural network (RNN)-based deep neural architectures, the work outlined in [26] applies the feature set constructed using AF to a fully convolutional neural architecture arranged in a U-Net structure. The input feature set in all of these works comprised a concatenation of magnitude spectrogram, IPD, and AF. In addition, the works in [6; 31; 32] involve MS-GLMB filtering to compute the labels distinguishing the target source from the interfering sources at every time frame, which were also instrumental in constructing T-V beamformer weights. The overall procedure outlined in [26] is expressed in Figure 1, for reference. Within the indoor environment, the area of potential target locations is divided into multiple angular sectors, each of which corresponds to an angular position of the target source at any time frame. The labelled RFS framework is helpful to demarcate the potential locations of the target speech source at different time frames from those of the interfering sources. This, in turn, helps to generate the training input dataset for the DL architecture, where the generated dataset reflects the effect of reverberation as well as the activity status of the target source. As the target and the interferers can be in different states of motion at different time frames, the dataset generated using the labelled RFS framework will reflect the state of the sources at different instants of time, which in turn helps to train the DL architecture more efficiently to predict the mask in a dynamic manner. The overall procedure can also be referred to as a form of beam zooming [20], where the applied DL architecture facilitates beam zooming on the potential location of the target source at each time frame. In this work, the angular location information of the target source is utilized to create the training dataset on which the proposed neural architecture is trained. The dataset is constructed from the magnitude spectrogram, while the label set (T-F mask specific to target source) to train the neural architecture is constructed from the spatial information of the target source estimated by the MS-GLMB filter. The procedure is depicted in Figure 2.  
Figure 1: Target speech enhancement with MS-GLMB filtering-assisted covariance mask prediction and GSC-based D-S beamformer [26]. While the AF used in previous works [11; 12; 13] is instrumental in achieving good target speech enhancement performance due to the construction of a feature set comprising spectro-spatial features corresponding to the target source, it may be helpful to use a simpler feature set (such as magnitude spectrogram) as input to the deep neural architecture, while still leveraging the spatial information of the moving target source at each frame to construct a suitable target label set to train the neural architecture. Hence, in this work, the proposed deep neural architecture is trained on the magnitude spectrograms of the received multi-channel reverberant mixture with the target source-specific T-F masks generated as labels, the latter having been generated by utilizing the spatial information of the multiple sources in motion. While direct estimation of a T-F mask from the complex spectra of received multichannel speech, as reported in [14; 15], can also lead to dereverberation, speaker separation and noise cancellation, the exploitation of speaker-dependent spatial features for the generation of a label set for the deep neural architecture can lead to improvement in signal selectivity, which in turn can improve the performance of the beamformer.