  Sound events in daily life carry rich information about the objective world.
The composition of these sounds affects the mood of people in a soundscape.
Most previous approaches only focus on classifying and detecting audio events
and scenes, but may ignore their perceptual quality that may impact humans'
listening mood for the environment, e.g. annoyance. To this end, this paper
proposes a novel hierarchical graph representation learning (HGRL) approach
which links objective audio events (AE) with subjective annoyance ratings (AR)
of the soundscape perceived by humans. The hierarchical graph consists of
fine-grained event (fAE) embeddings with single-class event semantics,
coarse-grained event (cAE) embeddings with multi-class event semantics, and AR
embeddings. Experiments show the proposed HGRL successfully integrates AE with
AR for AEC and ARP tasks, while coordinating the relations between cAE and fAE
and further aligning the two different grains of AE information with the AR.
