## 4 Experiments and Results
### Ablation Study  
In this subsection, we empirically demonstrate the effectiveness of our design choices. First, the impact of channel attention and spatial attention during spatial feature fusion is verified. Next, the performance of the group architecture blocks with varying numbers is analyzed, as well as that of the basic block structures. Finally, the effectiveness of combining FFA and BiGRU in our method is also assessed. Effect of Channel Attention and Spatial Attention for Spatial Feature Fusion. For the spatial feature learning module, channel and spatial attention mechanisms are introduced in each basic block of the group architecture blocks to focus more on the important channels and spatial regions. Meanwhile, to integrate multi-level features from the three group architecture blocks, channel and spatial attention mechanisms are used again to further \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **AUC** \\ \hline Short-chunk CNN [45] & 63.1\% & 63.9\% & 0.88 \\ CBAM [45] & 64.0\% & 65.9\% & 0.86 \\ BiLSTM + Attention [33] & 65.1\% & 65.8\% & 0.88 \\ ADFF [27] & 67.4\% & 68.0\% & 0.86 \\ SCMA [28] & 67.4\% & 69.3\% & 0.89 \\ CBAM + BiGRU & 65.1\% & 66.9\% & 0.89 \\ FFA + BiLSTM & 68.6\% & 69.8\% & 0.89 \\ Ours (FFA + BiGRU) & 69.8\% & 70.3\% & 0.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison between different models. Figure 5: Confusion matrix of the proposed model. fuse the concatenated features from these blocks. Therefore, to demonstrate the necessity and effectiveness of the attention mechanism for spatial feature fusion, the ablation study results are presented in Table 3 by removing either channel attention (named Model 1) or spatial attention (named Model 2). It shows that the accuracy drops to 68.6% and 67.4% when removing channel attention or spatial attention, respectively. Similarly, precision and the AUC performance also decrease when either of the two attention mechanisms is removed. This suggests that the combination of channel and spatial attention in spatial feature fusion is necessary and beneficial, even though these attention mechanisms have already been used in the group architecture blocks. Effect of varying the number of group architecture blocks and basic block structures. In the proposed model, we utilized three group architecture blocks to learn the multi-level spatial features, and each group architecture block consisted of 19 basic block structures. To verify that our selection is optimal, we explored the impact of varying the number of group architecture blocks and basic block structures on the classification performance, as shown in Tables 4 and 5. Table 4 reveals that different numbers of group architecture blocks result in discrepancies in accuracy performance. For the EMOPIA dataset, the model achieves the best performance when the number of group architecture blocks is 3. To further determine the optimal number of basic blocks within each group architecture, Table 5 displays the performance of models with varying numbers of basic blocks in a group architecture, with the number of group architecture blocks fixed at 3. It indicates that the model performs best when the number of basic blocks is 19. Effect of combining FFA and BiGRU. To further demonstrate the effectiveness of combining the spatial feature learning module (i.e., FFA) with the temporal feature learning module (i.e., BiGRU) in the proposed model, we conducted an ablation study by using only the spatial feature module, FFA, or solely, the temporal model, the BiGRU. As shown in Table 6, using either the FFA or BiGRU module alone may result in performance degradation. For instance, the accuracy of using only the FFA or BiGRU module alone is 64% and