  Audio captioning aims to automatically generate a natural language
description of an audio clip. Most captioning models follow an encoder-decoder
architecture, where the decoder predicts words based on the audio features
extracted by the encoder. Convolutional neural networks (CNNs) and recurrent
neural networks (RNNs) are often used as the audio encoder. However, CNNs can
be limited in modelling temporal relationships among the time frames in an
audio signal, while RNNs can be limited in modelling the long-range
dependencies among the time frames. In this paper, we propose an Audio
Captioning Transformer (ACT), which is a full Transformer network based on an
encoder-decoder architecture and is totally convolution-free. The proposed
method has a better ability to model the global information within an audio
signal as well as capture temporal relationships between audio events. We
evaluate our model on AudioCaps, which is the largest audio captioning dataset
publicly available. Our model shows competitive performance compared to other
state-of-the-art approaches.
