# Single-Channel Blind Source Separation of Spatial Aliasing Signal Based on Stacked-LSTM
); [EMAIL_ADDRESS] (Y.F.) Yan  
1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (C.G. ); [EMAIL_ADDRESS] (Y.F.) Yan  
1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (Y.F.) Zhaon  
1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (Y.F.) Zhaon  
1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (C.G. ); [EMAIL_ADDRESS] (Y.F.) Zhaon  
1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (C.G. ); [EMAIL_ADDRESS] (Y.F.) Zhaon  
1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (C.G. ); [EMAIL_ADDRESS] (Y.F.) Zhaon  
1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (C.G. ); [EMAIL_ADDRESS] (Y.F.) Zhaon  
1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (Y.F. ); [EMAIL_ADDRESS] (C.G. ); [EMAIL_ADDRESS] (Y.F.) Zhaon  
1 National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China; [EMAIL_ADDRESS] (M.Z. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (J.W. ); [EMAIL_ADDRESS] (Y.Fration method considers the physical basis [14]. However, deep learning does not. Deep learning only needs to extract useful information from big data to learn how to separate the source signal. However, these methods have their drawbacks. Large number of parameters significantly increase its computational cost and the separation accuracy performance is not good enough in Tasnet. The receiving field of one-dimensional convolution in the ConvTasNet network is smaller than the sequence length, and sequence-level modeling cannot be performed. The sampling rate of the original signal is used as the resolution in each feature map generated by convolution in Wave-U-Net, and the memory consumption is high. This paper proposes a single-channel blind source separation method for spatial aliasing signals based on deep learning--stacked-LSTM, which is a simple and effective method to organize RNN layers in a deep structure to model extremely long sequences. The latest research on signal separation based on deep learning has proves that time-domain methods are superior to traditional time-frequency-based methods [11; 12; 13]. In the time-frequency method, the time-frequency representation of the mixed signal is used to obtain the time-frequency domain mask of each source signal after training. This is a commonly used signal separation method, but this method has three shortcomings [15; 16; 17]: Separation accuracy problems caused by the suboptimality of frequency representation, long delay problems in calculating the spectrogram, and amplitude and phase decoupling problems. First, STFT is a general-purpose signal conversion, but it is not necessarily the best for the communication signal separation task, so it will lead to low separation accuracy.