## 5 Experimental Results
### Metrics Comparisons of Different Methods  
Table 3 shows the scores of speech quality metrics in the SECS-U-Net and the reference models on the open test set [30]. In Table 3, we reproduced Wiener [3], SEGAN [4], and SASEGAN [7]. The scores of other models from their papers. The scores of our model are the mean of multiple experiments. The experimental results show that the causal DEMUCS achieved the best results among all the referenced models. Compared to it, SECS-U-Net (\(L_{alpha}\)) achieves similar PESQ and STOI scores of 2.91 and 0.947, but other indicators were better than it. Compared with the latest Sinc-SEGAN [9], SECS-U-Net (\(L_{alpha}\)) acquires relative improvements of 11.11% and 14.92% over it on CSIG and COVL, respectively. In addition, compared with the classical Attention-Wave-U-Net [14], the proposed model has improved by 11.07%, 9.97%, 4.18% and 10.70% in PESQ, CSIG, CBAK and COVL, respectively. These results in Table 3 suggest that our model can effectively improve the quality and intelligibility of noisy speech, and the overall enhancement performance of the proposed model is better than referenced models.  
The metrics in Table 3 are objective evaluation indexes. The signal-to-artifacts ratio (SAR) and signal-to-distortion ratio (SDR) measures in [40] are selected to further evaluate the subjective quality of enhanced speech. Note that all these two metrics are better if higher. The average SSNR, SDR and SAR scores of our model and other reference models are recorded in Table 4. The experimental results show that Attention-Wave-U-Net achieves the best scores, although SECS-U-Net (\(L_{alpha}\)) is not as good as it, it can be found from Table 3 that the overall performance of our model is superior to it. SECS-U-Net (\(L_{alpha}\)) achieves better SAR and SDR scores of 19.92 and 18.91, respectively, compared with the latest DEMUCS (causal) and SASEGAN. In terms of SSNR, SECS-U-Net (\(L_{alpha}\)) acquires relative improvements of 11.84% over DEMUCS (causal) and 13.57% over SASEGAN. Table 4 shows that our model has less speech distortion.  
In Figure 4, the enhancement results of different models are further demonstrated by spectrograms. The results show that different models can effectively remove noise and achieve a good enhancement effect. However, by comparison, the proposed model has less residual noise and retains more details of speech, which further indicates that our model has less distortion and can achieve better enhancement performance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **PESQ** & **CSIG** & **CBAK** & **COVL** & **STOI** \\ \hline Noisy & 1.97 & 3.34 & 2.44 & 2.63 & 0.916 \\ Wiener [3] & 2.22 & 3.23 & 2.68 & 2.67 & 0.914 \\ SEGAN [4] & 2.24 & 3.47 & 2.93 & 2.84 & 0.931 \\ Wave-U-Net [13] & 2.40 & 3.52 & 3.24 & 2.96 & / \\ Attention-Wave-U-Net [14] & 2.62 & 3.91 & 3.35 & 3.27 & / \\ DEMUCS (causal) [15] & **2.93** & 4.22 & 3.25 & 3.52 & **0.950** \\ DSEGAN [6] & 2.35 & 3.55 & 3.10 & 2.93 & 0.933 \\ SASEGAN [7] & 2.41 & 3.62 & 3.06 & 2.99 & 0.935 \\ Sinc-SEGAN [9] & 2.86 & 3.87 & **3.66** & 3.15 & **0.950** \\ \hline SECS-U-Net (\(L_{alpha}\)) & **2.91** & **4.30** & **3.49** & **3.62** & **0.947** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Objective evaluation results of the proposed method and the reference methods (the bold indicates our metric scores and the metric scores that exceed ours in the reference models).