基于课程学习考虑事件学习难度的声音事件检测
Sound Event Detection Based on Curriculum Learning Considering Learning Difficulty of Events
摘要：
在传统的声音事件检测（SED）模型中，两种类型的事件（即，在声学场景中存在的事件和不发生的事件）被视为相同类型的事件。传统的SED方法不能有效地利用这两种类型的事件之间的差异。没有在声学场景中发生的声音事件的所有时间帧在场景中容易被认为是不活动的，即，事件容易训练。除了在声学场景中不活动之外，场景中存在的事件的时间帧必须被分类为活动的，也就是说，事件难以训练。为了利用训练难度，我们将课程学习应用到SED中，其中模型从容易到难以训练的事件进行训练。为了利用课程学习，我们提出了一个新的目标函数的SED，其中的事件是从容易到难以训练的事件进行训练。实验结果表明，该方法的F-分数提高了10.09个百分点相比，传统的基于二进制交叉熵的SED。
英文摘要：
In conventional sound event detection (SED) models, two types of events, namely, those that are present and those that do not occur in an acoustic scene, are regarded as the same type of events. The conventional SED methods cannot effectively exploit the difference between the two types of events. All time frames of sound events that do not occur in an acoustic scene are easily regarded as inactive in the scene, that is, the events are easy-to-train. The time frames of the events that are present in a scene must be classified as active in addition to inactive in the acoustic scene, that is, the events are difficult-to-train. To take advantage of the training difficulty, we apply curriculum learning into SED, where models are trained from easy- to difficult-to-train events. To utilize the curriculum learning, we propose a new objective function for SED, wherein the events are trained from easy- to difficult-to-train events. Experimental results show that the F-score of the proposed method is improved by 10.09 percentage points compared with that of the conventional binary cross entropy-based SED.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
