  Acoustic Scene Classification (ASC) is a challenging task, as a single scene
may involve multiple events that contain complex sound patterns. For example, a
cooking scene may contain several sound sources including silverware clinking,
chopping, frying, etc. What complicates ASC more is that classes of different
activities could have overlapping sounds patterns (e.g. both cooking and
dishwashing could have silverware clinking sound). In this paper, we propose a
multi-head attention network to model the complex temporal input structures for
ASC. The proposed network takes the audio's time-frequency representation as
input, and it leverages standard VGG plus LSTM layers to extract high-level
feature representation. Further more, it applies multiple attention heads to
summarize various patterns of sound events into fixed dimensional
representation, for the purpose of final scene classification. The whole
network is trained in an end-to-end fashion with back-propagation. Experimental
results confirm that our model discovers meaningful sound patterns through the
attention mechanism, without using explicit supervision in the alignment. We
evaluated our proposed model using DCASE 2018 Task 5 dataset, and achieved
competitive performance on par with previous winner's results.
