  Recently, many attention-based deep neural networks have emerged and achieved
state-of-the-art performance in environmental sound classification. The essence
of attention mechanism is assigning contribution weights on different parts of
features, namely channels, spectral or spatial contents, and temporal frames.
In this paper, we propose an effective convolutional neural network structure
with a multi-channel temporal attention (MCTA) block, which applies a temporal
attention mechanism within each channel of the embedded features to extract
channel-wise relevant temporal information. This multi-channel temporal
attention structure will result in a distinct attention vector for each
channel, which enables the network to fully exploit the relevant temporal
information in different channels. The datasets used to test our model include
ESC-50 and its subset ESC-10, along with development sets of DCASE 2018 and
2019. In our experiments, MCTA performed better than the single-channel
temporal attention model and the non-attention model with the same number of
parameters. Furthermore, we compared our model with some successful
attention-based models and obtained competitive results with a relatively
lighter network.
