In recent years, Event Sound Source Localization has been widely applied in
various fields. Recent works typically relying on the contrastive learning
framework show impressive performance. However, all work is based on large
relatively simple datasets. It's also crucial to understand and analyze human
behaviors (actions and interactions of people), voices, and sounds in chaotic
events in many applications, e.g., crowd management, and emergency response
services. In this paper, we apply the existing model to a more complex dataset,
explore the influence of parameters on the model, and propose a semi-supervised
improvement method SemiPL. With the increase in data quantity and the influence
of label quality, self-supervised learning will be an unstoppable trend. The
experiment shows that the parameter adjustment will positively affect the
existing model. In particular, SSPL achieved an improvement of 12.2% cIoU and
0.56% AUC in Chaotic World compared to the results provided. The code is
available at: this https URL

采用已有模型对更复杂的数据集进行应用，探索模型参数的影响，并提出了一种半监督改进方法 SemiPL。实验结果表明，参数调整对现有模型产生积极影响，特别是在混乱环境下，SSPL 相较于提供的结果，标准化 IoU 改进了 12.2％，AUC 改进了 0.56％。