Convolutive bounded component analysis algorithms for independent and dependent source separation

IEEE Transactions on Neural Networks and Learning Systems
Huseyin A Inan, Alper T Erdogan

Abstract

Bounded component analysis (BCA) is a framework that can be considered as a more general framework than independent component analysis (ICA) under the boundedness constraint on sources. Using this framework, it is possible to separate dependent as well as independent components from their mixtures. In this paper, as an extension of a recently introduced instantaneous BCA approach, we introduce a family of convolutive BCA criteria and corresponding algorithms. We prove that the global optima of the proposed criteria, under generic BCA assumptions, are equivalent to a set of perfect separators. The algorithms introduced in this paper are capable of separating not only the independent sources but also the sources that are dependent/correlated in both component (space) and sample (time) dimensions. Therefore, under the condition that the sources are bounded, they can be considered as extended convolutive ICA algorithms with additional dependent/correlated source separation capability. Furthermore, they have potential to provide improvement in separation performance, especially for short data records. This paper offers examples to illustrate the space-time correlated source separation capability through a copula distribution-based e...Continue Reading

References

Aug 27, 2005·IEEE Transactions on Neural Networks·Pando GeorgievAndrzej Cichocki
May 29, 2007·IEEE Transactions on Neural Networks·Frédéric VrinsMichel Verleysen

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