## 3 Experiments and Results
### Audio-Guided Space-Time Memory Network
#### 3.2.1 Dataset, Evaluation Metrics, and Hardware  
AVSbench is the first publicly released and most widely utilized audio-visual segmentation dataset. Ref-AVSbench represents a more recent and extensive dataset for audio-visual segmentation and possesses two significant characteristics: (1) It contains more audio and video frames and clips. The authors segmented each 10 s video into 10 equal 1 s clips, extracted the first frame of each clip as an image frame, and generated corresponding binary mask annotations. (2) An empty subset is introduced, where the target is silent and the associated mask labels are also empty. The extensive number of data frames in the Ref-AVS Bench dataset renders it particularly suitable for learning temporal information. Furthermore, the introduction of the empty subset enables models trained on this dataset to mitigate overfitting, suppress environmental noise, and reduce the mis-segmentation of unvoiced objects. Because of these characteristics, the Ref-AVSbench dataset was utilized for training in the experiments described in this section.  
AVSbench is the first publicly released and most widely used audio-visual segmentation dataset. Ref-AVSbench, a more recent and comprehensive dataset, offers two key features: (1) It includes a larger number of audio and video frames and clips. Each 10 s video is segmented into 10 equal 1 s clips, with the first frame of each clip extracted as an image frame and accompanied by binary mask annotations. (2) It introduces an empty  
Figure 5: The visualization is presented above. The top row presents the mask overlay images output by the model, while the bottom row displays the images annotated with overlay masks.  
subset where the target is silent, and the associated mask labels are also empty. The extensive data frames in Ref-AVSbench make it particularly suitable for learning temporal information, while the empty subset helps mitigate overfitting, suppress environmental noise, and reduce the mis-segmentation of unvoiced objects. Therefore, Ref-AVSbench was utilized for training in this study.  
Since the proposed method focuses solely on single-target tracking, when utilizing the Ref-AVSbench dataset, the data are preprocessed via sorting and filtering to retain only the single-source case. Additionally, it should be noted that while the Ref-AVSbench dataset includes an empty subset, most instances within the single-source subset are continuously voiced. Correspondingly, mask annotations predominantly label the target foreground for each segment. To enhance the audio-feature-extraction fusion module's sensitivity to changes in frequency and pitch intensity in the audio, as well as to mitigate the impact of environmental noise and interfering sound sources on target segmentation, modifications to the dataset are necessary. Specifically, certain audio segments are extracted and muted, with their corresponding mask labels designated as the background. Therefore, through these adjustments, learning samples capturing transitions from sounding to silence are incorporated, and the model's robustness with regard to its ability to recognize environmental sound variations is improved.  
The metrics employed in the experiments include the Jaccard Index, mean Intersection over Union (mIoU), and F-Score, which are the same as those used for evaluating the mask generator. A cloud server was used for the experiment. It is equipped with an AMD EPYC 9354 CPU, RAM of 64.4 GB, and an NVIDIA RTX 4090 GPU with VRAM of 24 GB. The operating system is Ubuntu 22.04, and the software environment includes Python 3.11.16, PyTorch 2.2.2, and Docker 26.1.0.