  Audio editing involves the arbitrary manipulation of audio content through
precise control. Although text-guided diffusion models have made significant
advancements in text-to-audio generation, they still face challenges in finding
a flexible and precise way to modify target events within an audio track. We
present a novel approach, referred to as PPAE, which serves as a general module
for diffusion models and enables precise audio editing. The editing is based on
the input textual prompt only and is entirely training-free. We exploit the
cross-attention maps of diffusion models to facilitate accurate local editing
and employ a hierarchical local-global pipeline to ensure a smoother editing
process. Experimental results highlight the effectiveness of our method in
various editing tasks.
