  Environmental sound classification (ESC) is a challenging problem due to the
complexity of sounds. The ESC 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 an 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. Experiments were conducted on
ESC-50 and ESC-10 datasets. Experimental results demonstrated the effectiveness
of the proposed method and achieved the state-of-the-art performance in terms
of classification accuracy.
