# 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
\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} **Entropy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Energy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Entropy-Based** \\ **Filtering with** \\ **Fuzzy Clustering** \\ \end{tabular} } \\ \cline{2-10}  & SNR & 1.310 & 15.003 & 14.758 & 14.846 & 14.863 & 14.803 & **14.872** \\ \cline{2-10}  & STOI & 0.911 & 0.926 & 0.927 & **0.942** & 0.941 & **0.942** & 0.941 \\ \cline{2-10}  & PESQ & 1.459 & 1.620 & **1.826** & 1.659 & 1.642 & 1.643 & 1.635 \\ \hline \multirow{2}{*}{Medium SNR} & SNR & 7.940 & 19.128 & 18.111 & **18.228** & 18.189 & 18.152 & 18.183 \\ \cline{2-10}  & STOI & 0.960 & 0.970 & **0.973** & 0.951 & 0.968 & 0.967 & 0.963 \\ \cline{2-10}  & PESQ & 2.043 & 2.432 & 2.172 & **2.448** & 2.368 & 2.364 & 2.371 \\ \hline \multirow{2}{*}{High SNR} & SNR & 16.490 & 21.919 & **21.047** & 21.028 & 20.997 & 20.966 & 20.991 \\ \cline{2-10}  & STOI & 0.985 & 0.984 & 0.982 & 0.980 & 0.982 & 0.979 & **0.985** \\ \cline{2-10}  & PESQ & 3.078 & 3.480 & **3.446** & 3.354 & 3.333 & 3.390 & 3.318 \\ \hline \multirow{2}{*}{Total Group} & SNR & 8.580 & 18.703 & \multicolumn{4}{c}{} & 18.049 \\ \cline{2-10}  & STOI & 0.952 & 0.959 & \multicolumn{4}{c}{} & 0.967 \\ \cline{2-10}  & PESQ & 2.194 & 2.511 & \multicolumn{4}{c}{} & 2.573 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of denoising performance only for the speech segments. showed the evidence that non-speech segments, which were excluded by the proposed strategy, were much more noisy than the speech segments. The result indicates that the overall quality of speech was improved, however, some speeches could be excluded by the speech segment detection. To complement this case, we could adjust a parameter of the speech segment detection method to increase the recall as shown in Section 3. For all the experiments, we measured the results for all the speech segment detection methods with the best threshold (or \(\lambda\)) setting for each group of SNRs to check their accuracy variation with a variety of SNRs. The result showed that the best speech segment detection method for denoising was different by the SNR group and the evaluation metric, which are represented in bold. This implied that we needed to select the most effective speech segment detection method of denoising for a target SNR and evaluation metric. We summarize the results that the proposed strategy was comparable to Wavenet-based denoising while reducing the execution time for denoising of Wavenet-based denoising significantly (i.e., by 40.06\(\sim\)50.76% as presented in Table 3). Figures 21 and 22 illustrate the denoised results of the proposed method representing two actual sample data. Figures 21a and 22a illustrate the original noisy-speech data, which were the target for denoising. Figures 21b and 22b illustrate the result of the speech segment detection. Here, we note that the non-speech segments were excluded while the noises in the speech segments are maintained. Figures 21c and 22c illustrate the denoised result of the proposed method; Figures 21d and 22d the clean speech data. We note that the noises were eliminated in Figures 21c and 22c compared to Figures 21b and 22b and the denoised result data became close to the clean speech data. We also indicate that the time axis of the proposed method was shortened by processing of the speech segment detection, which improved the denoising speed.