## 2 Method
### Spatiotemporal Feature Parameter Processing Module
#### 2.3.2 BiLSTM  
BiLSTM is based on LSTM, which is a type of recurrent neural network (RNN). The design goal of LSTM is to address the issues of vanishing and exploding gradients that commonly occur in RNNs when dealing with long sequence information [29]. LSTM introduces gate control mechanisms, including the \"forget gate\", \"input gate\", and \"output  
Figure 4: Structure of the ResNet network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Layer** & **Kernel** & **Stride** & **Channel** & **Activation Function** \\ \hline Conv & 3 & 1 & 32 & ReLU \\ BN & - & - & - & - \\ Conv & 3 & 1 & 32 & ReLU \\ Conv & 3 & 1 & 64 & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of the ResNet network.  
gate\" [30], which enable long-term memory when processing long sequence information. The structure of LSTM is illustrated in Figure 5.  
The computation steps of LSTM are as follows. Firstly, the previous timestep's state value, \(h_{t-1}\), and the current timestep's input information, \(x_{t}\), are used to calculate the values of three gates and the candidate memory cell, \(\widetilde{C}_{t}\).  
\[\begin{array}{l}f_{t}=\sigma\Big{(}W_{f}\times[h_{t-1},x_{t}]+b_{f}\Big{)}\\ i_{t}=\sigma(W_{i}\times[h_{t-1},x_{t}]+b_{i})\\ o_{t}=\sigma(W_{o}\times[h_{t-1},x_{t}]+b_{o})\\ \widetilde{C}_{t}=tanh(W_{c}\times[h_{t-1},x_{t}]+b_{c})\end{array} \tag{2}\]  
where \(x_{t}\) represents the current input; \(h_{t-1}\) represents the previous timestep's state value; \(\widetilde{C}_{t}\) represents the current timestep's candidate memory cell; \(f\) represents the forget gate; \(i\) represents the input gate; \(o\) represents the output gate; \(tanh\) refers to the hyperbolic tangent activation function; \(\sigma\) denotes the sigmoid activation function; \(W\) represents the weight matrix; and b represents the bias parameters.  
Next, the values of the forget gate, \(f_{t}\); input gate, \(i_{t}\); previous timestep's memory cell, \(C_{t-1}\); and candidate memory cell, \(\widetilde{C}_{t}\), are combined through element-wise multiplication and addition to update the memory cell, \(C_{t}\).  
\[C_{t}=f_{t}\times C_{t-1}+i_{t}\times\widetilde{C}_{t} \tag{3}\]  
Finally, the memory cell, \(C_{t}\), is processed through the \(tanh\) activation function and multiplied by the output gate, \(o_{t}\), to obtain the current timestep's state value, \(h_{t}\).  
\[h_{t}=o_{t}\times tanh(C_{t}) \tag{4}\]  
Due to the temporal correlation of noise, jitter, or other disturbances affecting air traffic control speech data [31], there is a continuous impact on the quality of speech data within a certain time range, rather than just instantaneously. In the spectrogram of the speech, this temporal correlation is manifested as persistent patterns or trends of these disturbances over time, rather than a completely random distribution. Merely relying on LSTM to extract forward information features from air traffic control speech is insufficient. Therefore, this paper utilizes a BiLSTM composed of both forward LSTM and backward LSTM to integrate the contextual information of air traffic control speech data. The structure of BiLSTM is illustrated in Figure 6.  
Figure 5: Structure of LSTM network structure.  
The computation process of BiLSTM involves performing forward propagation in the forward layer while simultaneously performing backward computation on the input sequence [32]. The final result is the stacking of outputs from both the forward LSTM and the backward LSTM. The design of BiLSTM in this paper is presented in Table 4.