  Environmental sound classification (ESC) is a challenging problem due to the
complexity of sounds. The classification performance is heavily dependent on
the effectiveness of representative features extracted from the environmental
sounds. However, ESC often suffers from the semantically irrelevant frames and
silent frames. In order to deal with this, we employ a frame-level attention
model to focus on the semantically relevant frames and salient frames.
Specifically, we first propose a convolutional recurrent neural network to
learn spectro-temporal features and temporal correlations. Then, we extend our
convolutional RNN model with a frame-level attention mechanism to learn
discriminative feature representations for ESC. We investigated the
classification performance when using different attention scaling function and
applying different layers. Experiments were conducted on ESC-50 and ESC-10
datasets. Experimental results demonstrated the effectiveness of the proposed
method and our method achieved the state-of-the-art or competitive
classification accuracy with lower computational complexity. We also visualized
our attention results and observed that the proposed attention mechanism was
able to lead the network tofocus on the semantically relevant parts of
environmental sounds.
