  Automated audio captioning aims to describe audio data with captions using
natural language. Existing methods often employ an encoder-decoder structure,
where the attention-based decoder (e.g., Transformer decoder) is widely used
and achieves state-of-the-art performance. Although this method effectively
captures global information within audio data via the self-attention mechanism,
it may ignore the event with short time duration, due to its limitation in
capturing local information in an audio signal, leading to inaccurate
prediction of captions. To address this issue, we propose a method using the
pretrained audio neural networks (PANNs) as the encoder and local information
assisted attention-free Transformer (LocalAFT) as the decoder. The novelty of
our method is in the proposal of the LocalAFT decoder, which allows local
information within an audio signal to be captured while retaining the global
information. This enables the events of different duration, including short
duration, to be captured for more precise caption generation. Experiments show
that our method outperforms the state-of-the-art methods in Task 6 of the DCASE
2021 Challenge with the standard attention-based decoder for caption
generation.
