  Audio inpainting aims to reconstruct missing segments in corrupted
recordings. Most of existing methods produce plausible reconstructions when the
gap lengths are short, but struggle to reconstruct gaps larger than about 100
ms. This paper explores recent advancements in deep learning and, particularly,
diffusion models, for the task of audio inpainting. The proposed method uses an
unconditionally trained generative model, which can be conditioned in a
zero-shot fashion for audio inpainting, and is able to regenerate gaps of any
size. An improved deep neural network architecture based on the constant-Q
transform, which allows the model to exploit pitch-equivariant symmetries in
audio, is also presented. The performance of the proposed algorithm is
evaluated through objective and subjective metrics for the task of
reconstructing short to mid-sized gaps, up to 300 ms. The results of a formal
listening test show that the proposed method delivers comparable performance
against the compared baselines for short gaps, such as 50 ms, while retaining a
good audio quality and outperforming the baselines for wider gaps that are up
to 300 ms long. The method presented in this paper can be applied to restoring
sound recordings that suffer from severe local disturbances or dropouts, which
must be reconstructed.
