## 1 Introduction  
Speech enhancement is very important in the field of speech processing. Its goal is to improve the quality and intelligibility of speech which disturbed by external noises. Speech enhancement is widely used in various applications, such as speech recognition, mobile communications, and hearing aids. Traditional speech enhancement algorithms include spectral-subtraction method [1, 2], Wiener filtering [1, 3] and subspace method [1], and so on. Recently, with the development of deep learning, speech enhancement has made major breakthroughs in deep learning-based methods. These methods include convolutional neural network (CNN), recurrent neural network (RNN), etc. Among them, speech enhancement based on convolutional U-Net has also attracted much attention. [PERSON] et al. applied Generative Adversarial Network (GAN) for speech enhancement (SEGAN) [4], which used the generator based on U-Net and worked end-to-end in time-domain. However, the denoising performance of SEGAN was mediocre. In [5], a SEGAN+ was proposed that added learning factor to skip connections of generator of SEGAN for improving denoising performance. Iterative SEGAN (ISEGAN) and Deep SEGAN (DSEGAN) were proposed by using multiple U-Net based generators to extract the same and different speech features, respectively [6]. However, multiple generators increased the model complexity and the number of parameters. In [7], a self-attention mechanism was proposed and coupled with the (de)convolutional layers of generator (SASEGAN). It paid attention to the temporal contexts of the speech. In [8], a new loss was defined by using the speech feature maps, which were obtained from the discriminator. The U-Net based generator in [9] adopts Sinc convolution, focusing on more underlying speech characteristics (Sinc-SEGAN). Furthermore, the U-Net was used to enhance noisy speech in time-frequency domain without adversarial training [10; 11]. They achieved a better enhancement effect, but the original noisy phase spectrum may affect denoising results. The Wave-U-Net [12] was also used to enhance speech in time-domain without adversarial training [13]. In addition [14], which is based on the structure of [13] added a local self-attention mechanism to each skip connection of U-Net. Ref. [15] proposed causal and non-causal U-Net models based on convolution and LSTM for enhancement (DEMUCS), and achieved comparable performance. The above researches show that U-Net has certain advantages in speech enhancement and deserves further exploration. In addition, the Compressed Sensing (CS) [16] also provides a new research direction for speech enhancement [17; 18; 19; 20]. CS has many constraints, and the sparse constraint on the signal is particularly important, but the general signal is only approximately sparse, which may lead to the degradation of reconstruction quality. Therefore, it is difficult to further improve the performance of CS. To solve these problems, [PERSON] et al. [21] pointed out that the signal is sparse as the most common structural assumption, but not the only one. Therefore, much research proposed CS based on deep learning [21; 22; 23; 24] and demonstrated the superiority of these methods in terms of signal sparsity and reconstruction. However, the application of CS based on deep learning in speech enhancement tasks still needs to be further explored. On the one hand, the U-Net can achieve better performance in speech enhancement. On the other hand, CS based on deep learning can effectively improve the quality of signal reconstruction by optimizing errors between different measurements. Recently, ref. [25] proposed a lightweight Attention mechanism, which helps to improve the performance of deep convolutional neural networks. We, therefore, propose a time-domain U-Net speech enhancement model combining lightweight Shuffle Attention mechanism and CS loss. We use the U-Net structure with 5 coding layers and 5 decoding layers as in [15].