  State-of-the-art audio captioning methods typically use the encoder-decoder
structure with pretrained audio neural networks (PANNs) as encoders for feature
extraction. However, the convolution operation used in PANNs is limited in
capturing the long-time dependencies within an audio signal, thereby leading to
potential performance degradation in audio captioning. This letter presents a
novel method using graph attention (GraphAC) for encoder-decoder based audio
captioning. In the encoder, a graph attention module is introduced after the
PANNs to learn contextual association (i.e. the dependency among the audio
features over different time frames) through an adjacency graph, and a top-k
mask is used to mitigate the interference from noisy nodes. The learnt
contextual association leads to a more effective feature representation with
feature node aggregation. As a result, the decoder can predict important
semantic information about the acoustic scene and events based on the
contextual associations learned from the audio signal. Experimental results
show that GraphAC outperforms the state-of-the-art methods with PANNs as the
encoders, thanks to the incorporation of the graph attention module into the
encoder for capturing the long-time dependencies within the audio signal. The
source code is available at https://github.com/LittleFlyingSheep/GraphAC.
