## 2 Methods
### Model Design
#### 2.2.2 Separation Network  
1. Separation Network  
The feature output \(e\) by the encoder is input into the separation network. First, the normalization processing is performed, and the original data is linearly transformed to scale its value to the interval of 0 and 1 to, thus, speed up the gradient descent and finding the optimal solution. Then, a 1-D convolution is applied to reduce the dimension of the channel and integrate the features of different data channels. Our research has also found that the algorithm complexity of the self-attention module is directly proportional to the square of the length of the input sequence. As mentioned in reference [4], processing smaller blocks in a dual-path framework mitigates the quadratic complexity of Transformers. In order to speed up the calculation of the attention mechanism module, we also segmented the input speech data and employed the dual path framework for operation. Assuming that the length of the speech sequence is \(L,L\) is divided into \(S\) blocks, the length of each block is \(K\), the sliding step between blocks is \(K//2\), and the obtained feature sequence is expressed as \(e^{(1)}\). Figure 1: Overall technical route of speech separation system. When the length of the last block of the sequence is less than \(K\), the padding method is utilized for filling. The advantage of this is to reduce the amount of calculation of matrix operations from the original O(\(L^{2}\)) to O(\(K^{2}\)) and O(\(S^{2}\)). The SuperFormer block mainly applies modules with different structures to model the dependence on block length \(K\) (intra-block) and block number \(S\) (inter-blocks) after the fusion of channel autocorrelation information and spatial autocorrelation information of speech sequence blocks. The module first extracts channel features of speech sequence blocks and then calculates the classification probability of each feature belonging to different speakers by Sigmoid classification function after extracting the channel features. This classification probability value is called the autocorrelation weight, and then the input information before feature extraction is multiplied by this weight to obtain the final output and is called the autocorrelation information on the channel. In the same way, the spatial features of speech sequence blocks are further extracted by the channel autocorrelation information. Then, the spatial autocorrelation information is obtained by classification, and the adaptive information on fusion channel and space is obtained. In addition, this paper represents the intra-block dependency information as local dependency information. Local dependency information is then fused with inter-block dependency information and represented as a global dependency. All in all, this model combines the local and global adaptive characteristics of the channel and space of speech blocks to improve the separation performance. After the \(e^{(2)}\) output from the SuperFormer Block is activated by PReLU, we design a 2D convolution calculation to obtain \(e^{(3)}\), then merge the \(S\) speech blocks with length of \(K\) back to the length before blocking to obtain \(e^{(4)}\). This feature is input into the forward feedback wise network (FFW), where different activation functions are exploited for joint calculation to control the output of features. The calculation process is as shown in Formulas (4)-(7). The internal design of FFW is shown in Figure 1.  
At the end of the separation network, a module for speaker enhancement is designed, which leverages the classification function on speaker features to classify different speaker features. We believe that this classification probability value is precisely derived from the autocorrelation information of speaker features. Thus, the module can enhance or suppress the features of each speaker, and finally obtains the mask \(e_{N}\) (\(N=1,2,\dots\) ) of each estimated source speech signal. The details of the speaker enhancement module will be described in Section 3.