## 4 Performance Evaluation
### Objective Quality Evaluation for Speech Enhancement  
The performance of each SE method was evaluated using the perceptual evaluation of speech quality (PESQ) [36] and short-time objective intelligibility (STOI) scores [37]. Figure 5 compares the average PESQ and STOI scores of noisy input speech and estimated speech according to the seven different SE methods using the matched evaluation dataset. Each bar in the figure was drawn after averaging the objective values over all speech signals, and the vertical line at the top of each bar denotes the standard deviation of each measurement. As shown in Figure 5a, the U-Net had a significantly better PESQ score than the SNMF and SEGAN and had similar performance to the DRNN. On the other hand, MTU-Net(IBM) had slightly lower and higher performance than the DRNN and U-Net, respectively. However, MTU-Net(IRM) had PESQ scores that were 0.17 and 0.26 higher than those of DRNN and U-Net, respectively. When comparing the SE performance considering STOI scores, as shown in Figure 5b, the deep learning-based methods, such as DRNN, SEGAN, and U-Net, achieved lower STOI scores than the unprocessed noisy speech because of the adverse effects on the speech components. In contrast, the proposed MTU-Net with the spectrogram, IBM, and IRM showed higher STOI scores by 0.88, 0.89, and 0.90, respectively, compared to the noisy input speech. Consequently, the proposed MTU-Net-based SE method with the IRM outperformed all other methods in terms of its PESQ and STOI scores. The performance evaluations of the SE methods were repeated under mismatched noise conditions. Figure 6 compares the average PESQ and STOI scores of the noisy input speech and the estimated speech according to the seven different SE methods applied to the matched evaluation dataset. Compared to Figure 5, which presents the evaluation results under the matched noise conditions, the NN-based SE methods, including DRNN, SEGAN, and U-Net, under the mismatched noise conditions had significantly degraded PESQ and STOI scores. However, the proposed MTU-Net with IRM provided similar PESQ and STOI scores under the matched noise conditions. This suggests that the proposed MTU-Net-based SE method with IRM is more robust than that with a spectrogram and IBM and is even more robust than other NN-based SE methods. Figure 5: Comparison of the objective quality measures for seven different speech enhancement methods applied to the matched evaluation dataset: (**a**) perceptual evaluation of speech quality (PESQ) and (**b**) short-time objective intelligibility (STOI). Next, the performance of all the SE methods was decomposed according to noise types and displayed in Tables 2 and 3 for PESQ and STOI, respectively. As shown in Table 2, the proposed MTU-Net-based SE methods provided higher PESQ scores than the others. MTU-Net(IRM) was the best in terms of PESQ for all noise types. In addition, MTU-Net(spec) and MTU-Net(IBM) showed comparable STOI scores to SNMF-based SE and much higher STOI scores than the other three NN-based SE methods of DRNN, SEGAN, and U-Net. MTU-Net(IRM) also had the highest STOI scores for all noise types. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Noise**} \\ \cline{2-6}  & **babble** & **f16** & **buccabber2** & **factory2** & **Average** \\ \hline Noisy & 2.26 & 2.25 & 2.19 & 2.47 & 2.29 \\ SNMF & 2.33 & 2.31 & 2.27 & 2.59 & 2.37 \\ DRNN & 2.50 & 2.68 & 2.68 & 2.83 & 2.67 \\ SEGAN & 2.13 & 2.43 & 2.41 & 2.57 & 2.40 \\ U-Net & 2.39 & 2.51 & 2.51 & 2.66 & 2.52 \\ MTU-Net(spec) & 2.60 & 2.79 & 2.78 & 2.94 & 2.78 \\ MTU-Net(IBM) & 2.23 & 2.76 & 2.55 & 2.70 & 2.56 \\ MTU-Net(IRM) & 2.77 & 2.95 & 2.98 & 3.02 & 2.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the perceptual evaluation of speech quality (PESQ) scores of seven different speech enhancement methods applied to the mismatched evaluation dataset.