Bayesian Separation of Discrete Sources via Gibbs Sampling
نویسندگان
چکیده
Source separation, one of the most recent domain of signal processing, consists in recovering signals mixed by an unknown transmission channel. Many approaches manage the separation using different techniques such as likelihood and information theory ([1],[6]) or high order statistics ([7]). This paper proposes a Bayesian approach to the problem of an instantaneous linear mixing, considering the sources are discrete. The powerful Gibbs sampling algorithm enables to recover binary sources, but also the mixing coefficients and noise levels with efficiency.
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