Bayesian single channel blind deconvolution using parametric signal and channel models

Abstract

This paper considers single channel blind deconvolution, in which a degraded observed signal is modelled as the convolution of a non-stationary source signal with a stationary distortion operator. Recovery of the source signal from the observed signal is achieved by modelling the source signal as a time-varying autoregressive process, the distortion operator by a IIR filter, and then using a Bayesian framework to estimate the parameters of the distorting filter, which can be used to deconvolve the observed signal. The paper also discusses how the non-stationary properties of the source signal allow the identification of the distortion operator to be uniquely determined.