## 3 Experiments and Results
### Mask Generator
#### 3.1.2 Experiments  
(1) Parameter determination  
We define the parameters \(\lambda_{1}\), \(\lambda_{2}\), and \(n\) for the mask-scoring mechanism used in model inference. As stated in the previous section, \(\lambda_{1}\) and \(\lambda_{2}\) denote the weighting parameters for area and similarity, respectively, and \(n\) indicates the total number of masks. Table 2 presents the typical parameter combinations and their corresponding indices derived from the experiments. The optimal combination of parameters is \(\lambda_{1}=0.1,\lambda_{2}=0.9\), \(n=3\); it is evident that the mask-scoring mechanism can enhance the performance of object segmentation. Due to the utilization of the Fastsam model based on CNN object detection networks, the overall recognition performance is superior. The masks of objects \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Layer Type** & **Kernel Size** & **Step Size** & **Kernel Size** & **Input Shape** & **Output Shape** \\ \hline Conv1-1d & 3 & 1 & 1 & 1 \(\times\) 22,050 & 64 \(\times\) 22,050 \\ MaxPool1d & 2 & 2 & - & 64 \(\times\) 22,050 & 64 \(\times\) 11,025 \\ Conv2-1d & 3 & 1 & 1 & 64 \(\times\) 11,025 & 128 \(\times\) 11,025 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of the audio feature extraction and fusion module. are more complete, the number of generated masks is reduced, and the area weight is smaller than the similarity weight. (2) Comparison experiments  
The ablation study was performed first. The parameter \(\lambda_{1}\) in the mask-scoring mechanism was set to 0, while \(\lambda_{2}\) was set to 1. This implies that only the audio-visual embedding correlation based on the Wav2 CLIP model is considered. Additionally, the parameter \(n\) was set to 1, meaning that only the mask with the highest correlation to the audio was output, and no mask concatenation was performed. Comparing the experimental metrics obtained without using the mask-scoring mechanism to those produced by the model within its optimal parameter configuration, we analyzed the impact of the mask-scoring mechanism on the model's performance. Subsequent to the ablation study, the performance metrics of the proposed mask generator were compared against those of other audio-visual segmentation methods, yielding the results presented in Table 3. Herein, TPAVI-R50 is the reference benchmark method for the AVSbench dataset [1]. SAM zero-shot represents the zero-shot learning approach leveraging the SAM and Ground-DINO models described in [23]. AV-SAM denotes the fine-tuning technique utilizing the SAM model as outlined in [13]. AL-Ref-SAM 2 refers to the method introduced in [14], which employs the SAM model for mask generation and the GPT-4 model for mask selection. The data in the table can be interpreted as follows: (1) The module incorporating the mask-scoring selection mechanism shows a significant improvement in the output index, validating its effectiveness. (2) The proposed mask generator outperforms the AV-SAM method, which relies on SAM model fine-tuning, indicating that it can serve as a lightweight alternative to SAM fine-tuning. (3) The proposed method exceeds the SAM zero-shot method in terms of the mean mIoU metric, demonstrating the efficacy of the introduced mask-scoring mechanism in enhancing output mask accuracy. (4) While the TPAVI-R50 benchmark and the AL-Ref-SAM 2 method (which integrates SAM and GPT-4 models) achieve higher accuracy and precision in object segmentation on the AVSbench dataset, the proposed method is more computationally efficient. It is better suited for platforms with limited resources due to its reduced dependence on heavy feature extraction networks like ResNet50 and advanced language models like GPT-4.