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- W2011679400 abstract "Gaussian process (GP) models are widely used in machine learning to account for spatial or temporal relationships between multivariate random variables. In this paper, we propose a formulation of underdetermined source separation in multidimensional spaces as a problem involving GP regression. The advantage of the proposed approach is firstly to provide a flexible means to include a variety of prior information concerning the sources and secondly to lead to minimum mean squared error estimates. We show that if the additive GPs are supposed to be locally-stationary, computations can be done very efficiently in the frequency domain. These findings establish a deep connection between GP and nonnegative tensor factorizations with the Itakura-Saito distance and we show that when the signals are monodimensional, the resulting framework coincides with many popular methods that are based on nonnegative matrix factorization and time-frequency masking." @default.
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- W2011679400 date "2011-06-01" @default.
- W2011679400 modified "2023-10-18" @default.
- W2011679400 title "Multi-dimensional signal separation with Gaussian processes" @default.
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- W2011679400 doi "https://doi.org/10.1109/ssp.2011.5967715" @default.
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