  Automated audio captioning (AAC) aims to describe the content of an audio
clip using simple sentences. Existing AAC methods are developed based on an
encoder-decoder architecture that success is attributed to the use of a
pre-trained CNN10 called PANNs as the encoder to learn rich audio
representations. AAC is a highly challenging task due to its high-dimensional
talent space involves audio of various scenarios. Existing methods only use the
high-dimensional representation of the PANNs as the input of the decoder.
However, the low-dimension representation may retain as much audio information
as the high-dimensional representation may be neglected. In addition, although
the high-dimensional approach may predict the audio captions by learning from
existing audio captions, which lacks robustness and efficiency. To deal with
these challenges, a fusion model which integrates low- and high-dimensional
features AAC framework is proposed. In this paper, a new encoder-decoder
framework is proposed called the Low- and High-Dimensional Feature Fusion
(LHDFF) model for AAC. Moreover, in LHDFF, a new PANNs encoder is proposed
called Residual PANNs (RPANNs) by fusing the low-dimensional feature from the
intermediate convolution layer output and the high-dimensional feature from the
final layer output of PANNs. To fully explore the information of the low- and
high-dimensional fusion feature and high-dimensional feature respectively, we
proposed dual transformer decoder structures to generate the captions in
parallel. Especially, a probabilistic fusion approach is proposed that can
ensure the overall performance of the system is improved by concentrating on
the respective advantages of the two transformer decoders. Experimental results
show that LHDFF achieves the best performance on the Clotho and AudioCaps
datasets compared with other existing models
