## 5 Experimental Results
### Ablation Experiment  
In our experimental stage, in order to evaluate the impact of CS loss on performance, we study the impact of different training objectives on the model enhancement results and record the average scores of each indicator for multiple experiments in Table 2. The loss adopted is indicated in parentheses. Both \(L_{all}\) and \(L_{alpha}\) said that the three training objectives were combined for training, which were, respectively, defined as \(L_{all}=L_{MAE}+L_{MSTFT}+L_{CS}\) and \(L_{alpha}=\alpha(L_{MAE}+L_{MSTFT})+(1-\alpha)L_{CS}\). Where \(\alpha\) is a weight factor to balance the contribution of each loss and set it to 0.8. Furthermore, the \"_No Att_\" means that the Shuffle Attention [25] is not used in our model, and \"_No Res_\" means that the Res_Block which we constructed is not used in the encoder and decoder. We only use ordinary convolution and transposed convolution for down-sampling and up-sampling. As in Table 2, no matter what type of loss is adopted, the proposed model can achieve better enhancement performance. Compared with SECS-U-Net (\(L_{MAE}\)), SECS-U-Net (\(L_{CS}\)) and SECS-U-Net (\(L_{MSTFT}\)), it is found that \(L_{MSTFT}\) can greatly improve the scores of PESQ, CSIG, CBAK and COVL, but the SSNR is lower, which is 8.70. It is proved that \(L_{MSTFT}\) can help our model to learn features of time-frequency and retain the speech information when training model in time-domain, but there may be residual noise in enhanced speech, which makes the SSNR lower. However, \(L_{MAE}\) and \(L_{CS}\) have better performance in improving SSNR. SECS-U-Net (\(L_{MAE}+L_{CS}\)), SECS-U-Net (\(L_{MAE}+L_{MSTFT}\)) and SECS-U-Net (\(L_{CS}+L_{MSTFT}\)), respectively, adopt the combination of two different losses as the training objective of our model. When \(L_{MAE}+L_{CS}\) was used, the SSNR is 9.43, and other indicators are inferior to the results of \(L_{MAE}+L_{MSTFT}\) and \(L_{CS}+L_{MSTFT}\), which further indicated that \(L_{MSTFT}\) is helpful to improve the denoising effect of model. The SSNR of SECS-U-Net (\(L_{MAE}+L_{MSTFT}\)) and SECS-U-Net (\(L_{CS}+L_{MSTFT}\)) are 9.13 and 9.51, respectively. For other indicators, the enhancement performance of SECS-U-Net (\(L_{MAE}+L_{MSTFT}\)) was slightly better than that of SECS-U-Net (\(L_{CS}+L_{MSTFT}\)). It indicates that when \(L_{MSTFT}\) is combined with \(L_{MAE}\) or \(L_{CS}\) as the training target, SSNR can be improved without degrading other speech evaluation criteria. It is also proved that \(L_{MAE}\) is better than \(L_{CS}\) in improving speech quality, but \(L_{CS}\) is slightly better than \(L_{MAE}\) in removing residual noise and improving SSNR of speech. Since all three training objectives help to improve the overall performance of our model, a compound objective is used to train model. It can not only enable our model to learn time-frequency characteristics, but also retain speech information and effectively suppress residual noise. Comparing \(L_{all}\) with \(L_{alpha}\), the enhancement performance of SECS-U-Net (\(L_{alpha}\)) is slightly better than SECS-U-Net (\(L_{all}\)). The comparison between SECS-U-Net (\(L_{alpha}\), \(No\ Att\)) and SECS-U-Net (\(L_{alpha}\)) shows that the attention mechanism is helpful to improve the scores of CBAK and SSNR, indicating that it can effectively suppress residual noise and improve the denoising ability of SECS-U-Net. In addition, comparing SECS-U-Net (\(L_{alpha}\), \(No\ Res\)) with SECS-U-Net (\(L_{alpha}\)) shows