## 6 Conclusions  
This paper proposed a time-domain U-Net structure for speech enhancement that applies CS loss and combines the Shuffle Attention mechanism. This attention can focus on the speech detailed features of the final output of the encoder and suppress irrelevant information. According to the CS based on deep learning, CS loss optimizes the error between measurements of clean speech and measurements of enhanced speech, which can improve the model enhancement performance. Furthermore, we construct the time-domain dilated residual blocks for down-sampling and up-sampling, it can capture the speech contextual information and detailed features. In the experimental part, the influence of different loss functions on the enhancement results was compared through ablation experiments. The experimental results also show that CS loss can be used as a training objective to train our model and achieve better denoising performance. Furthermore, the time-frequency loss can greatly improve the model enhancement performance. Compared with reference models trained under the same dataset, the proposed model can obtain higher speech quality scores with fewer parameters. When dealing with the noise outside the open dataset, the proposed model can also achieve better overall denoising performance. All the experimental analyses prove that our model can not only achieve a good enhancement effect but also has certain generalization abilities. In the future work, we will further study  
Figure 6: The spectrograms of speech enhanced by (**c**) Attention-Wave-U-Net [14], (**d**) SASEGAN [7] and (**e**) SECS-U-Net (\(L_{alpha}\)) on 560-Testset. The (**a**) and (**b**) are spectrograms of clean and noisy speech, respectively.  
the impact of CS loss applied to the time-frequency domain speech enhancement model, and study how to further improve the codec in U-Net to improve the speech enhancement performance.  
Conceptualization, Z.K. and Z.H.; methodology, Z.K.; software, Z.K. and C.L.; validation, Z.K. and C.L.; formal analysis, Z.K.; writing--original draft preparation, Z.K.; writing--review and editing, Z.H.; supervision, Z.H.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number 2018 YFC0823402, the Natural Science Foundation of Xinjiang Uygur Autonomous Region of China grant number 2017D01C044.  
Not applicable.  
The public dataset [30] used to evaluate our model is permanently available at: [[http://dx.doi.org/10.7488/ds/1356](http://dx.doi.org/10.7488/ds/1356)]([http://dx.doi.org/10.7488/ds/1356](http://dx.doi.org/10.7488/ds/1356)), accessed on 5 June 2021.  
The authors would like to thank the authors of [30] for the public dataset, as well as the editors and reviewers for their valuable comments.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} LSTM & long and short-term memory \\ CS loss & compressed sensing loss \\ GAN & Generative Adversarial Network \\ SEGAN & speech enhancement generative adversarial network \\ CS & Compressed Sensing \\ RIP & Restricted Isometry Property \\
1D-Conv & one-dimensional convolution \\ MSE & Mean Squared Error \\ MAE & Mean Absolute Error \\ STFT & Short-Time Fourier Transform \\ SNR & signal-to-noise ratio \\ PESQ & Perceptual Evaluation of Speech Quality \\ MOS & Mean opinion score \\ CSIG & MOS prediction of speech distortion \\ CBAK & MOS prediction of background noise interference \\ COVID & MOS prediction of overall effect \\ SSNR & Segmented SNR \\ STOI & Short-Time Objective Intelligibility \\ \end{tabular}