  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.
