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Blind source separation by nonstationarity of variance: a cumulant-based approach

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1 Author(s)
A. Hyvarinen ; Neural Networks Res. Centre, Helsinki Univ. of Technol., Finland

Blind separation of source signals usually relies either on the nonGaussianity of the signals or on their linear autocorrelations. A third approach was introduced by Matsuoka et al. (1995), who showed that source separation can be performed by using the nonstationarity of the signals, in particular the nonstationarity of their variances. In this paper, we show how to interpret the nonstationarity due to a smoothly changing variance in terms of higher order cross-cumulants. This is based on the time-correlation of the squares (energies) of the signals and leads to a simple optimization criterion. Using this criterion, we construct a fixed-point algorithm that is computationally very efficient

Published in:

IEEE Transactions on Neural Networks  (Volume:12 ,  Issue: 6 )