# A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection
## 4 Performance Evaluation
### Results and Discussion
#### 4.2.2 The Execution Time of Denoising  
Table 3 shows the execution times for Wavenet-based denoising and the proposed method. Here, we used all the files of the validation dataset. In measuring the execution time, we did not consider a variety of SNRs because its effects were negligible. The results reveal that the proposed method significantly reduced the execution time of Wavenet-based denoising by 40.06\(\sim\)50.76% according to the used speech segment detection method. This shows the significance of reducing the denoising time because the original Wavenet-based denoising required much more time in denoising (i.e., 3867 s) than even the original signal length (i.e., 2072.04 s) in our environmental setting. We note that the proposed method could reduce the denoising time significantly, which was less than the original signal length. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Speech Segment** \\ **Detection Methods** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Energy-Based** \\ **Filtering** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Entropy-Based** \\ **Filtering** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Fuzzy** \\ **Clustering** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Energy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} }} & \multicolumn{1}{c}{\multirow{2}{*}{\begin{tabular}{c} **Entropy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} }} \\ \cline{2-3} \cline{5-7}  & Threshold(\%)/\(\lambda\) & 0.8 & 0.5 & 40 & 50 & 80 \\ \cline{2-7} Low SNR & Recall & 0.779 & 0.839 & 0.876 & 0.864 & **0.891** \\ \cline{2-7} Group & Precision & **0.758** & 0.652 & 0.637 & 0.642 & 0.626 \\ \cline{2-7}  & F1 score & 0.751 & 0.719 & **0.727** & 0.726 & 0.725 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Medium SNR \\ **Group** \\ \end{tabular} } & Threshold(\%)/\(\lambda\) & 0.9 & 0.5 & 50 & 60 & 90 \\ \cline{2-7}  & Recall & **0.966** & 0.829 & 0.866 & 0.869 & 0.871 \\ \cline{2-7} Group & Precision & 0.705 & **0.814** & 0.790 & 0.786 & 0.786 \\ \cline{2-7}  & F1 score & 0.809 & 0.812 & **0.817** & 0.816 & **0.817** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} High SNR \\ **Group** \\ \end{tabular} } & Threshold(\%)/\(\lambda\) & 0.9 & 0.6 & 20 & 40 & 70 \\ \cline{2-7}  & Recall & 0.943 & 0.932 & **0.945** & 0.923 & 0.943 \\ \cline{2-7} Group & Precision & **0.933** & 0.914 & 0.902 & 0.919 & 0.902 \\ \cline{2-7}  & F1 score & **0.935** & 0.920 & 0.919 & 0.917 & 0.918 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Total \\ **Group** \\ \end{tabular} } & Threshold(\%)/\(\lambda\) & 0.9 & 0.6 & 30 & 50 & 80 \\ \cline{2-7}  & Recall & **0.959** & 0.941 & 0.911 & 0.890 & 0.910 \\ \cline{1-1} \cline{2-7}  & Precision & 0.773 & 0.773 & 0.806 & **0.817** & 0.803 \\ \cline{1-1} \cline{2-7}  & F1 score & 0.839 & 0.834 & **0.844** & 0.842 & **0.843** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The recall, precision, and F1 score by speech segment detection methods.