  Data-driven approaches hold promise for audio captioning. However, the
development of audio captioning methods can be biased due to the limited
availability and quality of text-audio data. This paper proposes a SynthAC
framework, which leverages recent advances in audio generative models and
commonly available text corpus to create synthetic text-audio pairs, thereby
enhancing text-audio representation. Specifically, the text-to-audio generation
model, i.e., AudioLDM, is used to generate synthetic audio signals with
captions from an image captioning dataset. Our SynthAC expands the availability
of well-annotated captions from the text-vision domain to audio captioning,
thus enhancing text-audio representation by learning relations within synthetic
text-audio pairs. Experiments demonstrate that our SynthAC framework can
benefit audio captioning models by incorporating well-annotated text corpus
from the text-vision domain, offering a promising solution to the challenge
caused by data scarcity. Furthermore, SynthAC can be easily adapted to various
state-of-the-art methods, leading to substantial performance improvements.
