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- W2117690194 abstract "We propose a novel approach to the blind source separation (BSS) that exploits frequency dependencies within a source. In contrast to conventional algorithms that separate the sources independently in each frequency bin, we assume that dependencies exist between frequency bins in a source signal. In this manner, we can reduce or eliminate the well-known frequency permutation problem. We derive the learning algorithm by defining a cost function as an extension of mutual information between multivariate random variables and by introducing a source prior that models the inherent frequency dependencies. This results in a simple form of a multivariate score function. In simulations and real recording experiments, we evaluate the performance of the proposed method and compare it against other well-known algorithms under various conditions. Our results indicate that modeling dependencies yields improved performance and robust scaling to higher number of sources and mixtures." @default.
- W2117690194 created "2016-06-24" @default.
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- W2117690194 date "2006-08-03" @default.
- W2117690194 modified "2023-09-26" @default.
- W2117690194 title "Frequency Domain Blind Source Separation Exploiting Higher-Order Dependencies" @default.
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- W2117690194 doi "https://doi.org/10.1109/icassp.2006.1661366" @default.
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