# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 4 Performance Evaluation
### Results and Discussion
#### 4.2.3 The Accuracy of Denoising  
Tables 4-6 show the SNR, STOI, and PESQ of the proposed method and Wavenet-based denoising with a variety of SNRs, respectively. In addition, we show the results of noisy speech data sets as a comparison. Here, we used all the files of the validation dataset. Each table shows the comparison results of the speech segments and non-speech segments targeting different segments. Table 4 shows the comparison of denoising performance for both speech and non-speech segments. Because our strategy excluded the non-speech segments, we attached the original non-speech segments without denoising to align its total length with the result of Wavenet-based denoising. Obviously, the overall accuracy of the proposed strategy was less than Wavenet-based denoising. However, this result indicated that the proposed strategy was quite effective (i.e., STOI shows better) even in the case where we utilized the original non-speech segments without denoising. Table 5 shows the comparison of denoising performance only for the speech segments. Here, we used only the speech segments in Wavenet-based denoising as well. The result showed that both methods had similar denoising performance for the speech segments. Table 6 compares the denoising performance for the final result of each method. Due to the speech segment detection in the proposed strategy, the overall length was different by the method, but it is worth showing the final result of the method. The overall improvement of the proposed strategy \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Denoising** \\ **Methods** \\ \end{tabular} } & \multirow{2}{*}{**Noisy**} & \multirow{2}{*}{\begin{tabular}{c} **Wavenet-Based** \\ **Denoising** [13] \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Energy-Based** \\ **Filtering** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Entropy-Based** \\ **Filtering** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Fuzzy** \\ **Filtering** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Energy-Based** \\ **Clustering** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Entropy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} } \\ \cline{3-3} \cline{6-10}  & & SNR & 0.915 & 14.552 & 5.408 & 7.554 & 7.669 & 7.639 & **8.349** \\ \cline{2-10}  & STOI & 0.868 & 0.864 & **0.848** & 0.845 & 0.843 & 0.846 \\ \cline{2-10}  & PESQ & 1.413 & 1.551 & **1.290** & 1.225 & 1.272 & 1.229 & 1.223 \\ \hline \multirow{2}{*}{Medium SNR} & SNR & 6.147 & 17.311 & **12.213** & 10.299 & 10.570 & 10.694 & 10.749 \\ \cline{2-10}  & STOI & 0.913 & 0.895 & 0.898 & 0.900 & **0.901** & 0.898 & 0.898 \\ \cline{2-10}  & PESQ & 1.757 & 1.824 & 1.515 & 1.531 & **1.577** & 1.520 & 1.519 \\ \hline \multirow{2}{*}{High SNR} & SNR & 13.555 & 19.639 & 15.504 & 15.742 & 15.811 & 15.567 & **15.820** \\ \cline{2-10}  & STOI & 0.951 & 0.914 & **0.943** & 0.942 & 0.942 & 0.941 \\ \cline{2-10}  & PESQ & 2.373 & 2.140 & **2.283** & 2.202 & 2.229 & 2.238 & 2.200 \\ \hline \multirow{2}{*}{Total Group} & SNR & 6.872 & 17.167 & \multicolumn{4}{c}{} & 12.127 \\ \cline{2-10}  & STOI & 0.910 & 0.891 & \multicolumn{4}{c}{} & 0.897 \\ \cline{2-10}  & PESQ & 1.847 & 1.838 & \multicolumn{4}{c}{} & 1.716 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison of denoising performance for both speech and non-speech segments.