  Acoustic matching aims to re-synthesize an audio clip to sound as if it were
recorded in a target acoustic environment. Existing methods assume access to
paired training data, where the audio is observed in both source and target
environments, but this limits the diversity of training data or requires the
use of simulated data or heuristics to create paired samples. We propose a
self-supervised approach to visual acoustic matching where training samples
include only the target scene image and audio -- without acoustically
mismatched source audio for reference. Our approach jointly learns to
disentangle room acoustics and re-synthesize audio into the target environment,
via a conditional GAN framework and a novel metric that quantifies the level of
residual acoustic information in the de-biased audio. Training with either
in-the-wild web data or simulated data, we demonstrate it outperforms the
state-of-the-art on multiple challenging datasets and a wide variety of
real-world audio and environments.
