  Most existing deep learning-based acoustic scene classification (ASC)
approaches directly utilize representations extracted from spectrograms to
identify target scenes. However, these approaches pay little attention to the
audio events occurring in the scene despite they provide crucial semantic
information. This paper conducts the first study that investigates whether
real-life acoustic scenes can be reliably recognized based only on the features
that describe a limited number of audio events. To model the task-specific
relationships between coarse-grained acoustic scenes and fine-grained audio
events, we propose an event relational graph representation learning (ERGL)
framework for ASC. Specifically, ERGL learns a graph representation of an
acoustic scene from the input audio, where the embedding of each event is
treated as a node, while the relationship cues derived from each pair of event
embeddings are described by a learned multidimensional edge feature.
Experiments on a polyphonic acoustic scene dataset show that the proposed ERGL
achieves competitive performance on ASC by using only a limited number of
embeddings of audio events without any data augmentations. The validity of the
proposed ERGL framework proves the feasibility of recognizing diverse acoustic
scenes based on the event relational graph. Our code is available on our
homepage (https://github.com/Yuanbo2020/ERGL).
