  Removing reverb from reverberant music is a necessary technique to clean up
audio for downstream music manipulations. Reverberation of music contains two
categories, natural reverb, and artificial reverb. Artificial reverb has a
wider diversity than natural reverb due to its various parameter setups and
reverberation types. However, recent supervised dereverberation methods may
fail because they rely on sufficiently diverse and numerous pairs of
reverberant observations and retrieved data for training in order to be
generalizable to unseen observations during inference. To resolve these
problems, we propose an unsupervised method that can remove a general kind of
artificial reverb for music without requiring pairs of data for training. The
proposed method is based on diffusion models, where it initializes the unknown
reverberation operator with a conventional signal processing technique and
simultaneously refines the estimate with the help of diffusion models. We show
through objective and perceptual evaluations that our method outperforms the
current leading vocal dereverberation benchmarks.
