论文标题
音频事件分类的本体感知框架
An Ontology-Aware Framework for Audio Event Classification
论文作者
论文摘要
音频事件分类的最新进展通常会忽略可作为先验信息的标签类之间的结构和关系。该结构可以通过本体论来定义，并在分类器中增强作为领域知识的形式。为了捕获标签之间的这种依赖性，我们提出了一个包含两个组成部分的本体感知神经网络：前馈本体层和图形卷积网络（GCN）。前馈本学层捕获了不同级别的本体论之间标记的依赖性。另一方面，GCN主要模型在本体级别内标记的相互依赖性结构。该框架在两个基准数据集上进行评估，用于单标签和多标签音频事件分类任务。结果表明，提出的解决方案功效可以捕获和探索本体关系并改善分类绩效。
Recent advancements in audio event classification often ignore the structure and relation between the label classes available as prior information. This structure can be defined by ontology and augmented in the classifier as a form of domain knowledge. To capture such dependencies between the labels, we propose an ontology-aware neural network containing two components: feed-forward ontology layers and graph convolutional networks (GCN). The feed-forward ontology layers capture the intra-dependencies of labels between different levels of ontology. On the other hand, GCN mainly models inter-dependency structure of labels within an ontology level. The framework is evaluated on two benchmark datasets for single-label and multi-label audio event classification tasks. The results demonstrate the proposed solutions efficacy to capture and explore the ontology relations and improve the classification performance.
