## 2 Methodology  
### Mask Generator  
The mask generator consists of three interconnected modules: a lightweight image segmentation model (Fastsam), an audio-visual modal alignment model (Wav2 CLIP), and a mask-scoring module. The Fastsam model is a lightweight segmentation model that achieves performance comparable to the SAM while attaining a running speed more than 50 times faster. It addresses the issue of high computational costs associated with the practical application of the SAM, enabling deployment on embedded platforms [20]. The Wav2 CLIP model achieves robust audio-visual modality alignment. It is lightweight, containing fewer than 50M parameters, which makes it well-suited for deployment on embedded platforms with constrained computational resources. Additionally, it can generate frame-level audio embeddings along the temporal axis, offering significant advantages for tasks requiring precise frame-level embeddings and enabling adaptive inference in video-related applications [21]. The lightweight architecture of the Fastsam and Wav2 CLIP models, combined with the simplicity and efficiency of the mask-scoring mechanism, ensures that the mask generator achieves high parameter efficiency while making the system scalable. However, the preliminary mask produced by the image segmentation model demonstrates fine-grained segmentation, dividing the target object into multiple parts. This outcome stems from the pre-trained model's ability to learn diverse object characteristics and knowledge from extensive datasets, enhancing its versatility in recognizing and generating masks for various objects. Excessively detailed segmentation not only complicates mask selection but also undermines the efficacy of correlation calculations in the audio-visual alignment model. To mitigate these challenges, two strategies can be implemented. The first approach involves enlarging the feature map in the model's detection head. This method is particularly effective in scenarios where the target mask size remains relatively stable, as it simplifies the problem and accelerates processing speed, albeit at the risk of overlooking smaller targets. The second approach focuses on filtering and stitching the output masks. While this method incurs higher computational costs and segments all target types, it offers more  
Figure 2: The proposed framework consists of two components: the audio-visual mask generator with a scoring mechanism and the audio-guided space–time memory network. First, paired images and audio clips are input into the mask generator. Subsequently, this module generates the mask of a sounding object and transmits it to the audio-guided space–time memory network. Lastly, the audio-guided space–time memory network leverages both historical and current masks to track the sounding object and determine whether to output the target mask based on the audio guidance. comprehensive information and is better suited for scenarios with substantial variations in target size. In most application scenarios, small targets are less frequent but tend to be more conspicuous, easier to observe, and meaningful when they do appear. Conversely, large targets, which are more common, are typically not divided into numerous parts. Therefore, this study adopts a second approach and introduces a mask-scoring mechanism to streamline the mask screening and stitching process. Specifically, the mask-scoring mechanism utilizes the full-instance mask segmentation map obtained from the Fastsam model to select and combine masks, thereby optimizing the generated masks and improving the accuracy and precision of object segmentation. As illustrated in Figure 3, the proposed mask-scoring module involves two critical processes. The first process calculates the correlation between audio and visual embeddings, which forms the foundation of audio-visual fusion. Specifically, this step identifies the mask that most accurately corresponds to the current input audio.