This paper proposes acoustic event detection (AED) with classifier chains, a new classifier based on the probabilistic chain rule. The proposed AED with classifier chains consists of a gated recurrent unit and performs iterative binary detection of each event one by one. In each iteration, the event's activity is estimated and used to condition the next output based on the probabilistic chain rule to form classifier chains. Therefore, the proposed method can handle the interdependence among events upon classification, while the conventional AED methods with multiple binary classifiers with a linear layer and sigmoid function have placed an assumption of conditional independence. In the experiments with a real-recording dataset, the proposed method demonstrates its superior AED performance to a relative 14.80% improvement compared to a convolutional recurrent neural network baseline system with the multiple binary classifiers.
翻译:本文建议使用分类链进行声学事件检测(AED),这是一个基于概率链规则的新分类器。 使用分类链的新分类器。 拟议的分类链的AED由封闭的经常性单元组成,对每个事件逐个进行迭代二进制检测。 在每次迭代中,根据概率链规则估计并使用该事件的活动来设定下一个产出,以形成分类链。 因此,拟议方法可以处理分类时的事件之间的相互依存性, 而使用具有线性层和类组功能的多个双进制分类器的常规系统,则设定了有条件的独立性假设。 在用真实记录数据集进行的实验中,拟议方法显示,与多个二进制分类器相比,其超常态神经网络基线系统的性能优于14.80%。