# Fusion Classification Method Based on Audiovisual Information Processing
## 4 Experimental Result and Analysis
### Comparative Test
#### 4.3.5 Compared with SOTA Methods  
In order to evaluate the validity of the proposed method, this paper compares it with both monomodal target recognition and the current mainstream multimodal fusion recognition algorithms. For monomodal audio recognition, the methods selected include PANNS_CNN [26] and ECAPAP_TDNN [27]. For monomodal image recognition, the methods selected are VIT [24] and VGG [9]. For multimodal fusion target recognition, the methods chosen include STNet [32], proposed by [PERSON] in 2024, the personalized CTD framework (PCTDF) [33], proposed by [PERSON] in 2024, AFT-SAM [34], proposed by [PERSON] in 2025, and PSFMMA [35], proposed by [PERSON] in 2025. Comparisons are made between the aforementioned algorithms and the M-AVFA proposed in this paper. The recognition performance of each algorithm was obtained by averaging the results of five experiments.  
As shown in Table 11, the proposed method in this paper achieves an average recognition rate of 84.32%, which outperforms other methods.  
1. Comparison with monomodal audio recognition methods: Compared with PANNS_CNN, the proposed method in this paper increases the average recognition rate by 7.9%. Compared with ECAPAP_TDNN, the proposed method increases the average recognition rate by 7.7%. This indicates that multimodal fusion methods can effectively compensate for the shortcomings of monomodal methods in audio feature extraction, thereby enhancing recognition performance.
2. Comparison with monomodal image recognition methods: Compared with ViT, the proposed method increases the average recognition rate by 7.5%. Compared with VGG, the proposed method increases the average recognition rate by 9.7%. The results show that multimodal fusion further enhances the accuracy of target recognition, especially in complex scenarios where the complementarity of multimodal information plays a significant role.
3. Comparison with the latest multimodal fusion recognition methods: Compared with the PCTDF method, the proposed method increases the average recognition rate by 2.6%. Compared with the STNet method, the recognition accuracy of the proposed method is increased by 2.4%. Compared with the AFT-SAM method, the proposed method increases the average recognition rate by 1.2%. Compared with the PSFMMA method, the recognition accuracy of the proposed method is increased by 0.9%. These results demonstrate that the proposed M-AVFA algorithm has significant advantages in multimodal fusion, effectively integrating audio and visual information to enhance target recognition performance in complex environments.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **G-Mean** \\ \hline PANNS\_CNN & 78.14 & 77.62 & 78.26 & 77.94 & 77.85 \\ ECAPAP\_TDNN & 78.31 & 78.12 & 77.85 & 77.98 & 78.06 \\ ViT & 78.42 & 77.94 & 78.32 & 78.13 & 78.06 \\ VGG & 76.86 & 77.21 & 76.26 & 76.73 & 76.84 \\ PCTDF & 82.21 & 82.12 & 82.41 & 82.26 & 82.16 \\ STNet & 82.74 & 82.67 & 82.76 & 82.71 & 82.74 \\ AFT-SAM & 83.31 & 83.26 & 83.54 & 83.40 & 83.56 \\ PSFMMA & 83.56 & 83.58 & 83.62 & 83.60 & 83.64 \\ M-AVFA & 84.32 & 84.36 & 84.58 & 84.45 & 84.41 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Quantitative results compared with SOTA method.  
In summary, the proposed M-AVFA algorithm performs exceptionally well in multimodal target recognition tasks, outperforming not only monomodal methods but also the latest multimodal fusion algorithms. This further validates the effectiveness and superiority of the M-AVFA algorithm in handling complex environment target recognition tasks.