  Recent text-to-audio generation techniques have the potential to allow novice
users to freely generate music audio. Even if they do not have musical
knowledge, such as about chord progressions and instruments, users can try
various text prompts to generate audio. However, compared to the image domain,
gaining a clear understanding of the space of possible music audios is
difficult because users cannot listen to the variations of the generated audios
simultaneously. We therefore facilitate users in exploring not only text
prompts but also audio priors that constrain the text-to-audio music generation
process. This dual-sided exploration enables users to discern the impact of
different text prompts and audio priors on the generation results through
iterative comparison of them. Our developed interface, IteraTTA, is
specifically designed to aid users in refining text prompts and selecting
favorable audio priors from the generated audios. With this, users can
progressively reach their loosely-specified goals while understanding and
exploring the space of possible results. Our implementation and discussions
highlight design considerations that are specifically required for
text-to-audio models and how interaction techniques can contribute to their
effectiveness.
