sense. A second approach, exemplified by nonnegative matrix factorization, is to impose structural constraints on the source signals. These structural constraints may be derived from a generative model of the signal, but are more commonly heuristics justified by good empirical performance. A common theme in the second approach is to impose some kind of low-complexity constraint on the signal, such as sparsity in some basis for the signal space. This approach can be particularly effective if one requires not the whole signal, but merely its most salient features. Methods There are different methods of blind signal separation: Principal components analysis Singular value decomposition Independent component analysis Dependent component analysis Non-negative matrix factorization Low-complexity coding and decoding Stationary subspace analysis Common spatial pattern Canonical correlation analysis See also Adaptive filtering Celemony Software#Direct Note Access Colin Cherry Deconvolution Factorial codes Infomax principle Segmentation (image processing) Speech segmentation References External links Explanation of Independent Component Analysis (ICA) A tutorial-style dissertation by Volker Koch that introduces message-passing on factor graphs to decompose EMG signals Blind source separation flash presentation Removing electroencephalographic artifacts by blind source separation Digital signal processing Speech processing de:Cocktail-Party-Effekt