## 4 Conclusions and Discussion  
In this research, we proposed a lightweight audio-visual segmentation method. This method comprises two key components: the first is an audio-visual fusion mask generator, and the second is an audio-guided video object-segmentation network based on space-time memory networks. By integrating these two components, the proposed method enables the completion of audio-visual segmentation for application scenarios as follows:  
1. To achieve lightweight audio-visual fusion object segmentation, a mask generator based on a pre-trained audio-visual modal alignment model is proposed. The pre-trained lightweight image segmentation model, Fastsam, is employed to generate initial masks. A mask-scoring mechanism is then designed to guide the refinement of the final mask output, thereby realizing audio-visual target mask generation. On the AVSbench test dataset, the proposed mask generator achieves an mIoU score of 54.5. Figure 6: 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. In order to achieve target tracking with audio-visual fusion mask prompts and address the issue of model mis-segmentation caused by environmental noise, a lightweight sound-controlled target tracking network is proposed. A space-time memory network is employed for target tracking and segmentation, while an audio feature extraction and fusion module is introduced to suppress environmental noise and interference sound sources. The experimental results demonstrate that the proposed method can effectively track targets under audio-visual fusion mask guidance, dynamically adjust the mask output in response to audio changes, and successfully suppress environmental noise. This network achieved an mIoU score of 53.2 on the Ref-AVSbench (S4) test dataset. 3. To evaluate the complete model, the mask generator was integrated with the audio-guided space-time memory network. The experimental results on the Ref-AVSbench (S4) test dataset demonstrate that the complete model is capable of recognizing and tracking vocal objects, providing a viable solution for audio-visual fusion target tracking. The mIoU score is 41.5. However, certain limitations remain and require further improvement in subsequent work. First, in the mask-generation approach, the mask-scoring mechanism can be optimized to enhance object segmentation accuracy. Specifically, a feedback-integrated mask-scoring mechanism could be designed to automatically determine the optimal parameter combination. Second, in AG-STMNet, the learning and representation capabilities of the audio feature extraction network can be further enhanced. Moreover, the fusion level between the audio information and mask decoder can be improved to strengthen the model's ability to suppress environmental noise and interfering sound sources. Data curation, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); investigation, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); methodology, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); software, Y.Z. ([PERSON]); visualization, Y.Z. ([PERSON]); validation, Y.Z. ([PERSON]) and Y.Z. ([PERSON]); writing--original draft preparation, Y.Z. ([PERSON]); writing--review, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research was funded by the Key Laboratory of Industrial Intelligence and System of Yunnan Province. Ethical review and approval were waived for this study for the following reasons. The research involved a non-invasive gait extraction study of quadruped animals based on computer vision. All of the data we used were from publicly available datasets and videos taken in zoos, without contact with the animals. The study did not cause any harm to any animals. According to the type of procedure used, no formal ethical approval was required. Not applicable.