# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 4 Experiments and Results
### Experiments on Simulated Data
#### 4.1.2 Experiment 2: The Performance versus Angle Difference  
This experiment evaluates the performance of SE methods versus the angle difference between the target and interference speakers. Let's define the angle difference as \(\Delta\phi\)= \(\phi_{s}-\phi_{l}\) and \(\Delta\theta=\theta-\theta_{i}\) \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Noise Conditions**} & \multirow{2}{*}{**SINR-Input (dB)**} & \multicolumn{2}{c}{**AVS-FMV [17] (dB)**} & \multicolumn{2}{c}{**AVS-SMASK (dB)**} \\ \cline{3-6}  & & **SINR-Out** & **Average** & **SINR-Out** & **Average** \\ \hline Trial 1 (\(n(t)\) = 0 and \(s_{i}(t)\
eq 0\) ) & \(-5\) & 4.96 & & 7.32 & \\ Trial 2 (\(n(t)\) = 0 and \(s_{i}(t)\
eq 0\) ) & 0 & 5.60 & 4.88 & 9.38 & \\ Trial 3 (\(n(t)\)= 0 and \(s_{i}(t)\
eq 0\) ) & 5 & 7.81 & 11.53 & & \\ Trial 4 (\(n(t)\)= 0 and \(s_{i}(t)\
eq 0\) ) & 10 & 11.15 & & 14.31 & \\ Trial 5 (\(n(t)\
eq 0\) and \(s_{i}(t)=0\) ) & \(-5\) & 4.77 & & 6.70 & \\ Trial 6 (\(n(t)\
eq 0\) and \(s_{i}(t)=0\)) & 0 & 5.51 & & 10.17 & \\ Trial 7 (\(n(t)\
eq 0\) and \(s_{i}(t)=0\)) & 5 & 6.76 & 4.97 & 13.03 & \\ Trial 8 (\(n(t)\
eq 0\) and \(s_{i}(t)=0\)) & 10 & 12.83 & & 16.55 & \\ Trial 9 (\(n(t)\
eq 0\) and \(s_{i}(t)\
eq 0\) ) & \(-5\) & 3.66 & & 4.70 & \\ Trial 10 (\(n(t)\
eq 0\) and \(s_{i}(t)\
eq 0\)) & 0 & 5.70 & 4.42 & 7.22 & \\ Trial 11 (\(n(t)\
eq 0\) and \(s_{i}(t)\
eq 0\)) & 5 & 7.10 & 10.46 & 6.66 & \\ Trial 12 (\(n(t)\
eq 0\) and \(s_{i}(t)\
eq 0\)) & 10 & 11.20 & & 14.27 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Output signal-to-interference plus noise ratio (SINR) under different noise conditions. (here, the subscripts \(s\) and \(i\) refer to the target speaker and the interference speaker, respectively). Obviously, the closer the interference speaker is to the target speaker, the speech enhancement is more limited. The experimental settings are as follows. (1) PESQ and LSD are used as metrics. (2) The parameters of algorithms are set as the same as those used in _Experiment 1_. (3) Without loss of generality, the SIR-input is set 0 dB, while SNR-input is set 10 dB. (4) We consider two cases. * Case 1: \(\Delta\theta\) is fixed and \(\Delta\phi\) is varied, (\(\theta_{1}\),\(\phi_{1}\)) = (45\({}^{\circ}\),0\({}^{\circ}\)), the DOA of the target speaker moves from (\(\theta_{s}\),\(\phi_{s}\)) = (45\({}^{\circ}\),0\({}^{\circ}\)) to (\(\theta_{s}\),\(\phi_{s}\)) (45\({}^{\circ}\),180\({}^{\circ}\)) with 20\({}^{\circ}\) increments. Hence, the angle difference \(\Delta\phi\) changes from 0\({}^{\circ}\) to 180\({}^{\circ}\) with 20\({}^{\circ}\) increments. Figure 6 shows the results of Case 1. From Figure 6, it is clear to see that when \(\Delta\phi\)\(\rightarrow\)0\({}^{\circ}\) (the target speaker moves closer to the interference speaker), for both algorithms, the PESQ drops significantly, and the LSD values are also big. These results indicate that the speech enhancement is very much limited if \(\Delta\phi\)\(\rightarrow\)0\({}^{\circ}\). However, when \(\Delta\phi\)\(>\) 20\({}^{\circ}\), the PESQ gradually increases, and LSD drops. It is quite encouraging to see that the performance of PESQ and LSD of our proposed AVS-SMASK algorithm is superior to that of the AVS-FMV algorithm for all of the angles. Moreover, our proposed AVS-SMASK algorithm has the absolute advantage when \(\Delta\phi\)\(\geq\) 40\({}^{\circ}\). * Case 2: \(\Delta\phi\) is fixed and \(\Delta\theta\) is varied, (\(\theta_{1}\),\(\phi_{1}\)) = (10\({}^{\circ}\),75\({}^{\circ}\)), the DOA of the target speaker moves from (\(\theta_{s}\),\(\phi_{s}\)) = (10\({}^{\circ}\),75\({}^{\circ}\)) to (\(\theta_{s}\),\(\phi_{s}\)) = (170\({}^{\circ}\),75\({}^{\circ}\)) with 20\({}^{\circ}\) increments. Then, the angle difference \(\Delta\theta\) changes from 0\({}^{\circ}\) to 160\({}^{\circ}\) with 20\({}^{\circ}\) increments. Figure 7 shows the results of Case 2.