  Most deep learning-based acoustic scene classification (ASC) approaches
identify scenes based on acoustic features converted from audio clips
containing mixed information entangled by polyphonic audio events (AEs).
However, these approaches have difficulties in explaining what cues they use to
identify scenes. This paper conducts the first study on disclosing the
relationship between real-life acoustic scenes and semantic embeddings from the
most relevant AEs. Specifically, we propose an event-relational graph
representation learning (ERGL) framework for ASC to classify scenes, and
simultaneously answer clearly and straightly which cues are used in
classifying. In the event-relational graph, embeddings of each event are
treated as nodes, while relationship cues derived from each pair of nodes are
described by multi-dimensional edge features. Experiments on a real-life ASC
dataset show that the proposed ERGL achieves competitive performance on ASC by
learning embeddings of only a limited number of AEs. The results show the
feasibility of recognizing diverse acoustic scenes based on the audio
event-relational graph. Visualizations of graph representations learned by ERGL
are available here (https://github.com/Yuanbo2020/ERGL).
