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: https://github.com/ly245422/SSPL

翻译：近年来，事件声源定位已在多个领域得到广泛应用。现有研究通常依赖对比学习框架，表现出令人印象深刻的性能。然而，所有工作均基于规模较大且相对简单的数据集。在许多应用场景（如人群管理与应急响应服务）中，理解并分析混乱事件中的人类行为（人物动作与互动）、语音及声音同样至关重要。本文将现有模型应用于更复杂的数据集，探究参数对模型的影响，并提出一种半监督改进方法SemiPL。随着数据量的增加及标签质量的影响，自监督学习将成为不可阻挡的趋势。实验表明，参数调整将对现有模型产生积极影响。特别地，SSPL在Chaotic World数据集上相较于已有结果实现了12.2%的cIoU和0.56%的AUC提升。代码已开源于：https://github.com/ly245422/SSPL