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Perception of auditory events is inherently multimodal relying on both audioand visual cues. A large number of existing multimodal approaches process eachmodality using modality-specific models and then fuse the embeddings to encodethe joint information. In contrast, we employ heterogeneous graphs toexplicitly capture the spatial and temporal relationships between themodalities and represent detailed information about the underlying signal.Using heterogeneous graph approaches to address the task of visually-awareacoustic event classification, which serves as a compact, efficient andscalable way to represent data in the form of graphs. Through heterogeneousgraphs, we show efficiently modelling of intra- and inter-modalityrelationships both at spatial and temporal scales. Our model can easily beadapted to different scales of events through relevant hyperparameters.Experiments on AudioSet, a large benchmark, shows that our model achievesstate-of-the-art performance.

Visually-aware Acoustic Event Detection using Heterogeneous Graphs