  Text-to-audio generation (TTA) produces audio from a text description,
learning from pairs of audio samples and hand-annotated text. However,
commercializing audio generation is challenging as user-input prompts are often
under-specified when compared to text descriptions used to train TTA models. In
this work, we treat TTA models as a ``blackbox'' and address the user prompt
challenge with two key insights: (1) User prompts are generally
under-specified, leading to a large alignment gap between user prompts and
training prompts. (2) There is a distribution of audio descriptions for which
TTA models are better at generating higher quality audio, which we refer to as
``audionese''. To this end, we rewrite prompts with instruction-tuned models
and propose utilizing text-audio alignment as feedback signals via margin
ranking learning for audio improvements. On both objective and subjective human
evaluations, we observed marked improvements in both text-audio alignment and
music audio quality.
