## 4 Computer Simulation Results
### MS-GLMB Filtering Based Multi-Source Tracking Filter  
As mentioned earlier, the multi-array measurements are processed by the MS-GLMB filter at each frame, whereby the source tracks comprising positions and labels are estimated. In this regard, it is useful to mention that a track can be considered as a function whose domain comprises the set of time instants at which the source exists. In general, a track corresponding to a particular source exists when the estimated positions of that source across the time frames can be associated with a common label. In practical scenarios, such as online multi-object tracking, the source may intermittently speak or disappear from the scene, giving rise to fragmented (or broken) tracks. Another scenario which frequently happens in online multi-object tracking is that which, when multiple sources are in very close proximity, the associated labels and their tracks may interchange. Figure 6 depicts the  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline \(\beta\) (in s\({}^{-1}\)) & 10 \\ \hline \(\theta\) (in ms\({}^{-1}\)) & 1 \\ \hline \(\phi\) (in ms) & 32 \\ \hline \(\sigma_{\mathcal{T}}\) (in ms\({}^{-1}\)) & \(\left[4.5,\ 4.5,\ 4.5\right]^{T}\) \\ \hline \end{tabular}
\end{table}
Table 2: Parameters of the [PERSON] model.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(r_{B}(l_{1})\) & 0.005 \\ \hline \(r_{B}(l_{2})\) & 0.005 \\ \hline \(r_{B}(l_{3})\) & 0.005 \\ \hline \(\mu_{B}^{(1)}\) & \(\left[5.0\ 1.0\ 1.8\ 0\ 0\ 0\right]^{T}\) \\ \hline \(\mu_{B}^{(2)}\) & \(\left[4.0.3\ 0.1\ 5.0\ 0\ 0\right]^{T}\) \\ \hline \(\mu_{B}^{(3)}\) & \(\left[2.5\ 0.5\ 1.5\ 0\ 0\right]^{T}\) \\ \hline \(P_{B}^{(1)}\) & \(\text{diag}(\left[0.15;0.15;0.15;0.15;0.15\right]^{T})\) \\ \hline \(P_{B}^{(2)}\) & \(\text{diag}(\left[0.15;0.15;0.15;0.15;0.15\right]^{T})\) \\ \hline \(P_{B}^{(3)}\) & \(\text{diag}(\left[0.15;0.15;0.15;0.15;0.15\right]^{T})\) \\ \hline \(P_{B}^{(3)}\) & \(\text{diag}(\left[0.15;0.15;0.15;0.15;0.15\right]^{T})\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the MS-GLMB filter.  
prediction of the (x-, y-) coordinates across all of the time frames by the MS-GLMB filtering at RT60 = 550 ms and compares against the ground truth positions.  
As observed from Figure 6, the application of MS-GLMB filtering to multi-source tracking in the acoustic domain leads to estimation of source tracks, which can still closely resemble the ground-truth tracks under the effects of noise and reverberation. This is due to the ability of the MS-GLMB filter to resolve the space-time permutation problem by jointly estimating the source labels and positions. This framework can account for noise, false positives and false negatives in the multi-array measurements (in this case, SRP-PHAT-based acoustic measurement at each array). Furthermore, source motions, labels as well as their appearances and disappearances are also incorporated into the formulation of the MS-GLMB filter for multi-source tracking. Because of these advantages, the MS-GLMB filter can guide the construction of input features in order to train the proposed neural framework, whereby the input feature set includes the directional information of the target source provided by the MS-GLMB filter.