  Automated audio captioning is a cross-modal translation task that aims to
generate natural language descriptions for given audio clips. This task has
received increasing attention with the release of freely available datasets in
recent years. The problem has been addressed predominantly with deep learning
techniques. Numerous approaches have been proposed, such as investigating
different neural network architectures, exploiting auxiliary information such
as keywords or sentence information to guide caption generation, and employing
different training strategies, which have greatly facilitated the development
of this field. In this paper, we present a comprehensive review of the
published contributions in automated audio captioning, from a variety of
existing approaches to evaluation metrics and datasets. We also discuss open
challenges and envisage possible future research directions.
