  Acoustic scenes are rich and redundant in their content. In this work, we
present a spatio-temporal attention pooling layer coupled with a convolutional
recurrent neural network to learn from patterns that are discriminative while
suppressing those that are irrelevant for acoustic scene classification. The
convolutional layers in this network learn invariant features from
time-frequency input. The bidirectional recurrent layers are then able to
encode the temporal dynamics of the resulting convolutional features.
Afterwards, a two-dimensional attention mask is formed via the outer product of
the spatial and temporal attention vectors learned from two designated
attention layers to weigh and pool the recurrent output into a final feature
vector for classification. The network is trained with between-class examples
generated from between-class data augmentation. Experiments demonstrate that
the proposed method not only outperforms a strong convolutional neural network
baseline but also sets new state-of-the-art performance on the LITIS Rouen
dataset.
