Abstract
AbstractIn this work, a novel probability distribution is proposed to model sparse directional data. The Directional Laplacian Distribution (DLD) is a hybrid between the linear Laplacian distribution and the von Mises distribution, proposed to model sparse directional data. The distribution’s parameters are estimated using Maximum-Likelihood Estimation over a set of training data points. Mixtures of Directional Laplacian Distributions (MDLD) are also introduced in order to model multiple concentrations of sparse directional data. The author explores the application of the derived DLD mixtures to cluster sound sources that exist in an underdetermined two-sensor mixture.KeywordsGaussian Mixture ModelIndependent Component AnalysisSource SeparationBlind Source SeparationCircular DataThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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