Acoustic Event Detection with Classifier Chains

Abstract

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 \red{binary detection} of each event one by one. In each iteration, the event's activity is estimated and used to condition the next \red{output} based on the probabilistic chain rule to form classifier chains. Therefore, the proposed method can handle the interdependence among events upon classification, \red{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.