# A Unified Speech Enhancement System Based on Neural Beamforming With Parabolic Reflector
## 3 Experiment and Results
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Volvo noise and speech interference**} \\ \hline
**Measure** & **SNR** & **Noisy** & **DSB** & **IRM-F** & **CIRM-F** & **IRM-BF** & **PR** & **PR-IRM-BF** \\ \hline \multirow{5}{*}{STOI} & \(-18\) dB & 0.361 & 0.393 & 0.618 & 0.479 & 0.705 & 0.619 & **0.808** \\  & \(-13\) dB & 0.456 & 0.488 & 0.722 & 0.603 & 0.806 & 0.744 & **0.874** \\  & \(-8\) dB & 0.562 & 0.589 & 0.800 & 0.704 & 0.870 & 0.843 & **0.910** \\  & \(-3\) dB & 0.677 & 0.678 & 0.868 & 0.805 & 0.904 & 0.913 & **0.930** \\  & \(2\) dB & 0.784 & 0.741 & 0.917 & 0.838 & 0.923 & **0.957** & 0.944 \\  & \(7\) dB & 0.868 & 0.780 & 0.949 & 0.923 & 0.930 & **0.981** & 0.947 \\ \hline \multirow{5}{*}{PESQ} & \(-18\) dB & 0.835 & 0.738 & 1.397 & 1.091 & 1.776 & 1.547 & **2.196** \\  & \(-13\) dB & 0.993 & 1.089 & 1.667 & 1.394 & 2.118 & 1.915 & **2.674** \\  & \(-8\) dB & 1.284 & 1.481 & 1.965 & 1.702 & 2.436 & 2.215 & **3.030** \\  & \(-3\) dB & 1.678 & 1.872 & 2.256 & 2.050 & 2.721 & 2.532 & **3.276** \\  & \(2\) dB & 1.982 & 2.265 & 2.533 & 3.357 & 2.877 & 2.871 & **3.486** \\  & \(7\) dB & 2.280 & 2.665 & 2.784 & 2.658 & 3.091 & 3.203 & **3.636** \\ \hline \multirow{5}{*}{fwSegSNR} & \(-18\) dB & \(-23.009\) & \(-22.330\) & \(-6.675\) & \(-2.596\) & \(-0.784\) & \(-8.496\) & **0.576** \\  & \(-13\) dB & \(-17.892\) & \(-17.217\) & \(-4.041\) & \(-0.757\) & 0.955 & \(-3.413\) & **0.818** \\ \cline{1-1}  & \(-8\) dB & \(-12.890\) & \(-12.223\) & \(-1.529\) & 1.016 & **1.718** & 1.588 & 1.083 \\ \cline{1-1}  & \(-3\) dB & \(-7.887\) & \(-7.227\) & 1.513 & 3.311 & 2.687 & **6.589** & 1.266 \\ \cline{1-1}  & \(2\) dB & \(-2.885\) & \(-2.234\) & 4.541 & 5.415 & 2.424 & **11.590** & 1.319 \\ \cline{1-1}  & \(7\) dB & 2.117 & 2.763 & 7.748 & 7.932 & 2.237 & **16.596** & 1.376 \\ \hline \hline \multicolumn{1}{c}{} & \multicolumn{6}{c}{**Pink noise and speech interference**} \\ \hline
**Measure** & **SNR** & **Noisy** & **DSB** & **IRM-F** & **CIRM-F** & **IRM-BF** & **PR** & **PR-IRM-BF** \\ \hline \multirow{5}{*}{STOI} & \(-18\) dB & 0.390 & 0.395 & 0.420 & 0.320 & 0.408 & 0.551 & **0.688** \\  & \(-13\) dB & 0.441 & 0.483 & 0.541 & 0.397 & 0.554 & 0.669 & **0.792** \\  & \(-8\) dB & 0.521 & 0.580 & 0.653 & 0.545 & 0.691 & 0.786 & **0.867** \\  & \(-3\) dB & 0.627 & 0.668 & 0.759 & 0.698 & 0.797 & 0.880 & **0.909** \\  & \(2\) dB & 0.736 & 0.731 & 0.830 & 0.811 & 0.856 & **0.941** & 0.934 \\  & \(7\) dB & 0.839 & 0.772 & 0.900 & 0.886 & 0.898 & **0.975** & 0.945 \\ \hline \multirow{5}{*}{PESQ} & \(-18\) dB & 1.003 & 0.746 & 1.085 & 1.007 & 1.072 & 1.314 & **1.738** \\  & \(-13\) dB & 1.028 & 1.043 & 1.190 & 1.040 & 1.267 & 1.587 & **2.203** \\ \cline{1-1}  & \(-8\) dB & 1.135 & 1.393 & 1.488 & 1.390 & 1.781 & 1.839 & **2.587** \\ \cline{1-1}  & \(-3\) dB & 1.375 & 1.783 & 1.807 & 1.776 & 2.200 & 2.149 & **2.916** \\ \cline{1-1}  & \(2\) dB & 1.715 & 2.177 & 2.062 & 2.143 & 2.422 & 2.486 & **3.185** \\ \cline{1-1}  & \(7\) dB & 1.988 & 2.554 & 2.350 & 2.512 & 2.775 & 2.837 & **3.469** \\ \hline \multirow{5}{*}{fwSegSNR} & \(-18\) dB & \(-23.746\) & \(-20.296\) & \(-10.597\) & \(-4.065\) & \(-3.235\) & \(-12.889\) & **0.258** \\ \cline{1-1}  & \(-13\) dB & \(-18.743\) & \(-15.300\) & \(-6.677\) & \(-1.282\) & \(-1.239\) & \(-7.889\) & **0.265** \\ \cline{1-1}  & \(-8\) dB & \(-13.740\) & \(-10.305\) & \(-3.933\) & \(-0.037\) & 0.053 & \(-2.889\) & **0.388** \\ \cline{1-1}  & \(-3\) dB & \(-8.737\) & \(-5.308\) & \(-0.544\) & 1.862 & 1.3672 & **2.110** & 0.7513 \\ \cline{1-1}  & \(2\) dB & \(-3.749\) & \(-0.322\) & 2.200 & 3.881 & 1.698 & **7.101** & 1.039 \\ \cline{1-1}  & \(7\) dB & 1.267 & 4.688 & 5.628 & 5.927 & 1.764 & **12.111** & 1.243 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._