# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 3 Results
### Speech Enhancement  
This section presents experimental results of the proposed speech enhancement for hearing aids using DNNs in each noise type. Table 1 shows the results of applying the speech enhancement algorithm (After) according to each objective evaluation measure. The quality of speech enhancement in noisy signals with large SNRs produced better results than those with low SNRs, in all noise types. In particular, the subway noise, fan noise, and laundry noise exhibited higher PESQ, STOI, and OQCM, and lower LLR. The reason is that the three noises had regular patterns and the babble noise interfered less when speech enhancement was applied using the DNNs. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Noise**} & \multirow{2}{*}{\
\begin{tabular}{c} **SNR** \\ **(dB)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **PESQ Score** \\ **Before** \\ \end{tabular} } & \multicolumn{2}{c}{**STOI Score**} & \multicolumn{2}{c}{**OQCM Score**} & \multicolumn{2}{c}{**LLR Score**} \\  & & **Before** & **After** & **Before** & **After** & **Before** & **After** & **Before** & **After** \\ \hline \multirow{4}{*}{Café} & 0 & 1.086 & 1.315 & 0.642 & 0.759 & 1.415 & 1.850 & 0.956 & 0.624 \\  & 5 & 1.195 & 1.578 & 0.750 & 0.832 & 1.731 & 2.255 & 0.729 & 0.450 \\  & 10 & 1.419 & 1.966 & 0.837 & 0.889 & 2.117 & 2.727 & 0.530 & 0.317 \\  & 15 & 1.802 & 2.460 & 0.907 & 0.934 & 2.608 & 3.251 & 0.359 & 0.214 \\ \hline \multirow{4}{*}{Car} & 0 & 1.071 & 1.472 & 0.712 & 0.820 & 1.533 & 2.257 & 0.690 & 0.225 \\  & 5 & 1.165 & 1.788 & 0.780 & 0.862 & 1.812 & 2.629 & 0.507 & 0.164 \\  & 10 & 1.369 & 2.162 & 0.845 & 0.901 & 2.162 & 3.025 & 0.357 & 0.122 \\  & 15 & 1.710 & 2.586 & 0.901 & 0.936 & 2.591 & 3.442 & 0.241 & 0.091 \\ \hline \multirow{4}{*}{Fan} & 0 & 1.075 & 1.456 & 0.685 & 0.808 & 1.400 & 2.107 & 1.002 & 0.469 \\  & 5 & 1.179 & 1.758 & 0.768 & 0.856 & 1.705 & 2.503 & 0.765 & 0.338 \\  & 10 & 1.396 & 2.128 & 0.839 & 0.898 & 2.094 & 2.930 & 0.547 & 0.235 \\  & 15 & 1.758 & 2.624 & 0.901 & 0.934 & 2.571 & 3.435 & 0.364 & 0.163 \\ \hline \multirow{4}{*}{Laundry} & 0 & 1.066 & 1.455 & 0.644 & 0.808 & 1.336 & 2.034 & 1.011 & 0.577 \\  & 5 & 1.149 & 1.756 & 0.753 & 0.869 & 1.631 & 2.448 & 0.787 & 0.421 \\  & 10 & 1.352 & 2.159 & 0.840 & 0.915 & 2.013 & 2.914 & 0.578 & 0.303 \\  & 15 & 1.711 & 2.635 & 0.903 & 0.947 & 2.491 & 3.411 & 0.396 & 0.210 \\ \hline \multirow{4}{*}{Library} & 0 & 1.080 & 1.460 & 0.671 & 0.793 & 1.484 & 2.054 & 1.056 & 0.686 \\  & 5 & 1.167 & 1.694 & 0.758 & 0.849 & 1.725 & 2.377 & 0.875 & 0.555 \\  & 10 & 1.458 & 2.134 & 0.854 & 0.905 & 2.193 & 2.895 & 0.614 & 0.391 \\  & 15 & 1.780 & 2.514 & 0.908 & 0.938 & 2.596 & 3.299 & 0.450 & 0.294 \\ \hline \multirow{4}{*}{Office} & 0 & 1.059 & 1.349 & 0.629 & 0.770 & 1.355 & 1.893 & 1.083 & 0.678 \\  & 5 & 1.136 & 1.609 & 0.729 & 0.834 & 1.633 & 2.278 & 0.863 & 0.520 \\  & 10 & 1.316 & 1.962 & 0.822 & 0.888 & 1.992 & 2.714 & 0.642 & 0.382 \\  & 15 & 1.649 & 2.404 & 0.896 & 0.931 & 2.448 & 3.194 & 0.447 & 0.270 \\ \hline \multirow{4}{*}{Restaurant} & 0 & 1.070 & 1.322 & 0.614 & 0.755 & 1.245 & 1.814 & 1.347 & 0.784 \\  & 5 & 1.143 & 1.610 & 0.739 & 0.842 & 1.528 & 2.237 & 1.126 & 0.599 \\  & 10 & 1.310 & 1.987 & 0.847 & 0.905 & 1.888 & 2.697 & 0.878 & 0.448 \\  & 15 & 1.632 & 2.474 & 0.922 & 0.948 & 2.354 & 3.221 & 0.635 & 0.319 \\ \hline \multirow{4}{*}{Subway} & 0 & 1.094 & 1.486 & 0.715 & 0.814 & 1.554 & 2.215 & 0.774 & 0.331 \\  & 5 & 1.215 & 1.811 & 0.780 & 0.858 & 1.847 & 2.611 & 0.578 & 0.241 \\ \cline{1-1}  & 10 & 1.453 & 2.189 & 0.843 & 0.898 & 2.228 & 3.029 & 0.406 & 0.168 \\ \cline{1-1}  & 15 & 1.825 & 2.637 & 0.900 & 0.933 & 2.690 & 3.479 & 0.266 & 0.117 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of speech quality by proposed noise reduction algorithm based on noise classification.