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Independent component analysis (ICA) is currently the most popularly used approach to blind source separation (BSS), the problem of recovering unknown source signals when their mixtures are observed but the actual mixing process is unknown. Many ICA algorithms assume that a fixed set of source signals consistently exists in mixtures throughout the time-series to be examined. However, real-world signals...
Most of the existing algorithms for blind source separation (BSS) assume that the number of sources is known and constant for all samples. Real situations, however, often have difficult non-stationarity such that each source signal abruptly switches to appear or disappear and hence the number of sources varies with time. In this article, we propose a noisy independent component analysis (ICA) algorithm...
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