Joint Bayesian removal of impulse and background noise

We present a method for the removal of noise including non-Gaussian impulses from a signal. Impulse noise is removed jointly a homogenous Gaussian noise floor using a Gabor regression model. The problem is formulated in a joint Bayesian framework and we use a Gibbs MCMC sampler to estimate parameters. We show how to deal with variable magnitude impulses using a shifted inverse gamma distribution for their variance. Our results show improved signal to noise ratios and perceived audio quality by explicitly modelling impulses with a discrete switching process and a new heavy-tailed amplitude model.