## 4 Experiments
### Performance with Competing Speakers  
The experimental results are summarized in terms of GER in Table 1 and in terms of MAE in Table 2. We report the results of the SRP-PHAT algorithm, the original cWMM-based DoA estimation algorithm (cWMM), the cWMM-based algorithm with the target Time-frequency Selection BLSTM network (cWMM-TF), and the cWMM based algorithm using the Oracle target binary mask (cWMM-ORC). A threshold of 0.6 was heuristically chosen to convert the network outputs to binary values. \(K=181\) was set to evaluate the source azimuth in a \(1^{\circ}\) step. The results were averaged over different SNRs.  
It is shown that the GER and MAE scores followed similar trends. For both SRP-PHAT and cWMM, there was a general performance degradation as the reverberant time increased, while a special case was the \(-5\) dB SIR, where the GER and MAE were highest in the 160-ms case. The reason was possibly that the less reverberant the mixture was, the higher probability that the estimated DoA corresponded to the stronger interference. Clearly, their performance improved rapidly as the SIR increased. In the case of 0 dB SIR, there was around a 50% GER, meaning that the algorithms could not distinguish the target speaker from the interference without any prior information and just made a guess from the two competing directions, which should be at least \(15^{\circ}\) away because of the experimental setup. As the SIR went up, the algorithms output the DoA of the stronger source in the mixture, but still, the performance was poor. With the oracle target mask, the cWMM-ORC  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Noise Type**} & \multicolumn{2}{c}{**N0**} & \multicolumn{2}{c}{**N1**} \\ \hline  & **SIR** & \(\mathbf{-5}\) & **0** & **5** & \(\mathbf{-5}\) & **0** & **5** \\ \hline \multirow{4}{*}{160 ms} & SRP-PHAT & 0.90 & 0.56 & 0.30 & 0.82 & 0.64 & 0.28 \\  & cWMM & 0.76 & 0.48 & 0.28 & 0.74 & 0.59 & 0.28 \\  & cWMM-TF & 0.43 & 0.24 & **0.15** & 0.48 & 0.25 & 0.14 \\  & cWMM-ORC & 0.09 & 0.08 & 0.07 & 0.07 & 0.10 & 0.05 \\ \hline \multirow{4}{*}{360 ms} & SRP-PHAT & 0.83 & 0.47 & 0.34 & 0.82 & 0.51 & 0.34 \\  & cWMM & 0.75 & 0.47 & 0.37 & 0.75 & 0.51 & 0.33 \\  & cWMM-TF & **0.42** & 0.34 & 0.18 & **0.42** & 0.28 & 0.16 \\  & cWMM-ORC & 0.08 & 0.06 & 0.09 & 0.06 & 0.07 & 0.10 \\ \hline \multirow{4}{*}{610 ms} & SRP-PHAT & 0.78 & 0.56 & 0.29 & 0.82 & 0.62 & 0.25 \\  & cWMM & 0.70 & 0.54 & 0.28 & 0.73 & 0.57 & 0.28 \\ \cline{1-1}  & cWMM-TF & 0.48 & **0.22** & **0.15** & 0.52 & **0.23** & **0.13** \\ \cline{1-1}  & cWMM-ORC & 0.11 & 0.08 & 0.07 & 0.08 & 0.10 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The GER (100%) of the algorithms in different test environments with competing speakers. The bold numbers indicate the best results excluding cWMM-ORC for each SIR. TF, Time-Frequency; ORC, Oracle.  
Figure 4: An example for oracle investigation. The sub-figures are, respectively, **(a)** the mixture spectrum, (**b**) the ideal binary mask of the target, **(c)** the reference spectrum, **(d)** the original cWMM weights, **(e)** the cWMM weights after time-frequency selection, and **(f)** the averaged weights before (orig) and after (after) processing.  
performed quite robustly in all the test cases and achieved a GER of 8% and an MAE of around 2\({}^{\circ}\). The cWMM-ORC was somewhat the upper bound of the cWMM-TF algorithm. Using the masks estimated from the BLSTM network, the cWMM-TF achieved on average 26% GER and 14.54\({}^{\circ}\) MAE in the 0-dB SIR case, which were, respectively, a 51% and 55% relative reduction of the original scores without time-frequency selection. The time-frequency selection processing suppressed the interference and effectively benefited the localization accuracy.