Blind Source Separation using Space–Time Independent Component Analysis

Davies, Mike E.; Jafari, Maria G.; Abdallah, Samer A.; Plumbley, Mark D.

doi:10.1007/978-1-4020-6479-1_3

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Each row of W is a 1 × M un-mixing vector w i related to one of extracted signals. Many strategies have been used for BSS [6,[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. One of them is projection pursuit [6] that is based on the central limit theorem (CLT) [7].…”

Section: Introductionmentioning

confidence: 99%

A theoretical discussion on the foundation of Stone’s blind source separation

Khosravy

Asharif

Yamashita

2010

SIViP

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This paper discusses the theoretical foundation of Stone's BSS (Stone in Neural Comput 13:1559-1574; Stone in Independent Component Analysis: A Tutorial Introduction, A Bradford Book, London, 2004), and it proposes a novel BSS approach based on second-order statistics of the responses of two different linear filters to source signals. The proposed approach which includes Stone's BSS as a special case helps us to understand how generalized eigenvalue decomposition (GEVD) concludes separating vectors in Stone's BSS. It obtains the separating vectors by simultaneous diagonalization of covariance matrices of two different linear filters responses to the mixtures. The two employed linear filters are selected dependent on source signals structures under the assumption that they have different responses to source signals. Here, two FIR filters with coefficients selected in an opposite probabilistic way have been suggested for the proposed BSS. The proposed BSS method has been compared with Stone's BSS, SOBI and AMUSE over speech and image mixtures in different noise levels.

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Section: Introductionmentioning

confidence: 99%

A theoretical discussion on the foundation of Stone’s blind source separation

Khosravy

Asharif

Yamashita

2010

SIViP

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Space-Time ICA and EM Brain Signals

Davies

James

Wang

Independent Component Analysis and Signal Separation

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An adaptive stereo basis method for convolutive blind audio source separation

et al. 2008

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We consider the problem of convolutive blind source separation of stereo mixtures, where a pair of microphones records mixtures of sound sources that are convolved with the impulse response between each source and sensor. We propose an Adaptive Stereo Basis (ASB) source separation method for such convolutive mixtures, using an adaptive transform basis which is learned from the stereo mixture pair.The stereo basis vector pairs of the transform are grouped according to the estimated relative delay between the left and right channels for each basis, and the sources are then extracted by projecting the transformed signal onto the subspace corresponding to each group of basis vector pairs. The performance of the proposed algorithm is compared with FD-ICA and DUET under different reverberation and noise conditions, using both objective distortion measures and formal listening tests. Preprint submitted to Elsevier Science 10 August 2007NOTICE: this is the author's version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in : Neurocomputing 71(10-12), 2087-2097, June 2008. doi: 10.1016/j.neucom.2007 The results indicate that the proposed stereo coding method is competitive with both these algorithms at short and intermediate reverberation times, and offers significantly improved performance at low noise and short reverberation times.

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Blind Source Separation using Space–Time Independent Component Analysis

Cited by 3 publications

References 34 publications

A theoretical discussion on the foundation of Stone’s blind source separation

A theoretical discussion on the foundation of Stone’s blind source separation

Space-Time ICA and EM Brain Signals

An adaptive stereo basis method for convolutive blind audio source separation

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