An iterative inversion approach to blind source separation

Sergio Cruces-Alvarez, Andrzej Cichocki, Luis Castedo-Ribas

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


In this paper we present an iterative inversion (II) approach to blind source separation (BSS). It consists of a quasi-Newton method for the resolution of an estimating equation obtained from the implicit inversion of a robust estimate of the mixing system. The resulting learning rule includes several existing algorithms for BSS as particular cases giving them a novel and unified interpretation.It also provides a justification of the Cardoso and Laheld step size normalization [12]. The II method is first presented for instantaneous mixtures and then extended to the problem of blind separation of convolutive mixtures. Finally, we derive the necessary and sufficient asymptotic stability conditions for both the instantaneous and convolutive methods to converge.

Original languageEnglish
Pages (from-to)1423-1437
Number of pages15
JournalIEEE Transactions on Neural Networks
Issue number6
Publication statusPublished - 2000
Externally publishedYes


  • Adaptive signal processing
  • Blind source separation (BSS)
  • Equivariant learning algorithms
  • Higher order statistics
  • Independent component analysis (ICA)
  • Multichannel blind deconvolution (MBD)


Dive into the research topics of 'An iterative inversion approach to blind source separation'. Together they form a unique fingerprint.

Cite this