# Fusion Classification Method Based on Audiovisual Information Processing
## 4 Experimental Result and Analysis
### Comparative Test
#### 4.3.3 Ablation Test  
Ablation experiments are used to evaluate the contribution of specific components or features to the performance of a model by removing or modifying them one by one. In order to deeply explore the importance of each component in the M-AVFA algorithm, a series of ablation experiments were carefully designed in this paper. By systematically removing or replacing key modules in the algorithm (feature extraction module, fusion strategy) and comparing experimental results in different configurations, this paper aims to quantify the impact of each component on the overall performance, thereby revealing the central role of each part of the algorithm and its contribution to the target recognition task. **Experiment 1: Ablation experiment of feature extraction module**  
This study starts with the feature extraction module. Specifically, the experiment initially focuses on the feature extraction module. The image feature extraction module and the audio feature extraction module, serving as the core components of the visual and auditory modalities, respectively, are each removed and subjected to comparative tests. The performance of the model on the dataset is observed. The experimental results are shown in Table 7.  
The experimental results show that when the feature extraction module of any mode is removed, the performance of the model on the test set decreases significantly. The results show that both image features and sound features play an indispensable role in the target recognition task, and they complement each other to provide a rich source of information for the model. When the fusion module is removed, the performance of the model decreases most significantly. This finding further validates the core role of acousto-optic information fusion in complex environment target recognition. By integrating multimodal information, the fusion module effectively makes up for the limitations of a single mode in a specific scene and significantly improves the robustness and accuracy of the model. **Experiment 2: Ablation experiment of fusion strategy**  
In order to deeply explore the influence of different fusion strategies on multimodal target recognition performance, this study designed a series of ablation experiments to systematically analyze the effectiveness and applicability of various fusion strategies. Specifically, the experiment revolves around the key modules in the M-AVFA algorithm and observes the impact on model performance by replacing or removing specific fusion strategies one by one. The experiment compares the three fusion strategies proposed in this paper, namely Feature fusion, and Decision fusion, and M-AVFA. The results are shown in Table 8.  
Feature fusion can make full use of the original information of each mode by directly splicing multi-mode features, but it is easy to introduce noise in complex scenes. Decision fusion combines the decision results of each mode by means of weighting or voting, which has strong robustness; however, in some cases, it may not be able to make full use of the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reserved Module** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **G-Mean** \\ \hline Audio feature extraction & 0.79 & 0.78 & 0.80 & 0.79 & 0.78 \\ Image feature extraction & 0.79 & 0.80 & 0.81 & 0.80 & 0.79 \\ M-AVFA & 0.84 & 0.84 & 0.86 & 0.85 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Feature extraction module ablation experiment. complementary information of multiple modes. The fusion strategy proposed in this paper has advantages. **Experiment 3: Comparison of feature fusion methods**  
In order to deeply explore the influence of different feature fusion methods on multimodal target recognition performance, a series of ablation experiments were designed to systematically analyze the effectiveness and applicability of various feature fusion methods.