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Gaussian Processes for Source Separation: Pseudo-likelihood Maximization  

Park, Sun-Ho (포항공과대학 컴퓨터공학과)
Choi, Seung-Jin (포항공과대학 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.35, no.7, 2008 , pp. 417-423 More about this Journal
Abstract
In this paper we present a probabilistic method for source separation in the case here each source has a certain temporal structure. We tackle the problem of source separation by maximum pseudo-likelihood estimation, representing the latent function which characterizes the temporal structure of each source by a random process with a Gaussian prior. The resulting pseudo-likelihood of the data is Gaussian, determined by a mixing matrix as well as by the predictive mean and covariance matrix that can easily be computed by Gaussian process (GP) regression. Gradient-based optimization is applied to estimate the demixing matrix through maximizing the log-pseudo-likelihood of the data. umerical experiments confirm the useful behavior of our method, compared to existing source separation methods.
Keywords
source separation; temporal structure of source; Gaussian process; maximum pseudo-likelihood;
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