Diffusion Posterior Sampling for Informed Single-Channel Dereverberation
Jean-Marie Lemercier Simon Welker Timo Gerkmann
We present in this paper an informed single-channel dereverberation method
based on conditional generation with diffusion models. With knowledge of the
room impulse response, the anechoic utterance is generated via reverse
diffusion using a measurement consistency criterion coupled with a neural
network that represents the clean speech prior. The proposed approach is
largely more robust to measurement noise compared to a state-of-the-art
informed single-channel dereverberation method, especially for non-stationary
noise. Furthermore, we compare to other blind dereverberation methods using
diffusion models and show superiority of the proposed approach for large
reverberation times. We motivate the presented algorithm by introducing an
extension for blind dereverberation allowing joint estimation of the room
impulse response and anechoic speech. Audio samples and code can be found
online (https://uhh.de/inf-sp-derev-dps).