论文标题
与分类器链的声学事件检测
Acoustic Event Detection with Classifier Chains
论文作者
论文摘要
本文提出了带有分类器链的声学事件检测（AED），这是一种基于概率链规则的新分类器。提议的带有分类器链的AED由一个封闭式的复发单元组成，并对每个事件进行迭代二进制检测。在每次迭代中，估算事件的活动并用于根据概率链规则来调节下一个输出以形成分类器链。因此，所提出的方法可以在分类时处理事件之间的相互依赖性，而具有线性层和sigmoid函数的多个二进制分类器的常规AED方法已将有条件独立性的假设放置。在使用真实录制数据集的实验中，该方法证明了与带有多个二元分类器的卷积复发性神经网络基线系统相比，其优于相对14.80％的AED性能提高了14.80％。
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.
