标题： 开放环境中的声音事件检测集成置信度校准 显示英文标题

标题： Ensemble Confidence Calibration for Sound Event Detection in Open-environment

摘要： 声音事件检测（SED）在具有明确事件类别的受控环境中取得了显著进展。 然而，现实世界的应用通常发生在开放环境中。 在这些情况下，当前的方法往往产生过于自信的预测，并且缺乏适当的方法来衡量不确定性。 这限制了它们在新情况下的适应能力和表现。 为了解决这个问题，我们首次在SED中使用集成方法，以提高对域外（OOD）输入的鲁棒性。 我们提出了一种称为基于能量的开放世界Softmax（EOW-Softmax）的置信度校准方法，该方法有助于系统更好地处理未知场景中的不确定性。 我们进一步通过调整预测，将EOW-Softmax应用于声音出现和重叠检测（SOD）。 这样，模型在保持检测重叠事件能力的同时变得更加适应。 实验表明，我们的方法提高了开放环境中的性能。 它减少了过度自信，并增强了处理OOD情况的能力。 显示英文摘要

摘要： Sound event detection (SED) has made strong progress in controlled environments with clear event categories. However, real-world applications often take place in open environments. In such cases, current methods often produce predictions with too much confidence and lack proper ways to measure uncertainty. This limits their ability to adapt and perform well in new situations. To solve this problem, we are the first to use ensemble methods in SED to improve robustness against out-of-domain (OOD) inputs. We propose a confidence calibration method called Energy-based Open-World Softmax (EOW-Softmax), which helps the system better handle uncertainty in unknown scenes. We further apply EOW-Softmax to sound occurrence and overlap detection (SOD) by adjusting the prediction. In this way, the model becomes more adaptable while keeping its ability to detect overlapping events. Experiments show that our method improves performance in open environments. It reduces overconfidence and increases the ability to handle OOD situations.