## 2 Related Work  
### Audio-Visual Sound Source Localization (AV-SSL)  
Audio-visual sound source localization techniques can be categorized into traditional methods and learning-based methods. The traditional ones mainly combine SSL with SLAM [9; 10; 11; 12]. This type of method typically begins with estimating DoA using multi-channel audio data from microphone arrays and then calculates the relative distances between the sound sources and the robot referenced by SLAM. For instance, [PERSON] et al. [10] used SEVD-MUSIC to estimate the DoA of the sound and RGB-D SLAM for mapping and localization. Subsequently, the extended Kalman filter was utilized to track the movement of the source. This method successfully located one static or moving speaker in the real world. Similarly, [PERSON] et al. [11] made use of GSVD-MUSIC and EKF-SLAM to localize and track the sound sources. In addition, combined with OpenPose, a monocular camera-based human pose estimator, their system focused more on human-robot interaction. In contrast with the above two studies using just one robot, [PERSON] et al. [9] proposed a method using two robots referenced via RTAB-Map, a graph-based SLAM, to localize the sound source. In [9], each robot was equipped with a tower-shaped 16-mic 3D array. To locate and track the sound source, SRP-PHAT-HSDA and triangulation were used in their method. It is obvious that the aforementioned studies improve the accuracy of sound source localization by means of SLAM. In order to improve the robustness of SLAM to dynamic environments, an approach based on direct-path relative transfer functions was presented by [PERSON] et al. [12] to eliminate the moving objects.  
With the development of machine learning, some researchers have been seeking learning-based solutions to locate sound sources. For instance, [PERSON] et al. [22] introduced a self-supervised learning-based probabilistic spatial model for AV-SSL. To train this model, 360\({}^{\circ}\) images with multi-channel audio signals were fed into visual and auditory DNNs. [PERSON] et al. [23] replaced the DNN with SVM and trained the models using 1-channel audio from a single microphone to simultaneously estimate the DoA and the distance between the sound source and the microphone. Regrettably, although the above systems can accurately locate multiple sound sources at the same time, they are unable to identify the sources and focus on a specific target, which is actually a highly desired capability of robot audition in cocktail party environments. To address this issue, SELDnet was proposed in [21] for Sound Event Localization and Detection (SELD) of overlapping sources to provide the robot with the ability to identify the temporal activities of each sound event and estimate their respective spatial location trajectories when active. In recent years, SELD has received increasing attention from researchers [24; 25; 26]. However, even the most advanced techniques of SELD only have a success rate of around 41.6% in sound event detection and an angular error of approximately 18.5\({}^{\circ}\) in sound event localization [20]. Due to the low performance, these techniques cannot be applied directly to resolve the cocktail party problem.