Source Separation with Gaussian Process Models

In this paper we address a method of source separation in the case where sources have certain temporal structures. The key contribution in this paper is to incorporate Gaussian process (GP) model into source separation, representing the latent function which characterizes the temporal structure of a source by a random process with Gaussian prior. Marginalizing out the latent function leads to the Gaussian marginal likelihood of source that is plugged in the mutual informationbased loss function for source separation. In addition, we also consider the leave-one-out predictive distribution of source, instead of the marginal likelihood, in the same framework. Gradient-based optimization is applied to estimate the demixing matrix through the mutual information minimization, where the marginal distribution of source is replaced by the marginal likelihood of the source or its leave-one-out predictive distribution. Numerical experiments confirm the useful behavior of our method, compared to ...

• Type: Conference
• Year: 2007
• Where: ECML
• Authors: Sunho Park, Seungjin Choi
• Keywords: ECML 2007, Leave-one-out Predictive Distribution, Machine Learning, Marginal Likelihood, Source Separation