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Independent vector analysis with a generalized multivariate Gaussian source prior for frequency domain blind source separation

Lookup NU author(s): Dr Mohsen Naqvi, Professor Jonathon Chambers

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Abstract

Independent vector analysis (IVA) is designed to retain the dependency within individual source vectors, while removing the dependency between different source vectors. It can theoretically avoid the permutation problem inherent to independent component analysis (ICA). The dependency in each source vector is retained by adopting a multivariate source prior instead of a univariate source prior. In this paper, a multivariate generalized Gaussian distribution is adopted as the source prior which can exploit frequency domain energy correlation within each source vector. As such, it can utilize more information describing the dependency structure and provide improved source separation performance. This proposed source prior is suitable for the whole family of IVA algorithms and found to be more robust in applications where non-stationary signals are separated than the one preferred by Lee. Experimental results on real speech signals confirm the advantage of adopting the proposed source prior on three types of IVA algorithm.

Publication metadata

Author(s): Liang Y, Harris J, Naqvi SM, Chen G, Chambers JA

Publication type: Article

Publication status: Published

Journal: Signal Processing

Year: 2014

Volume: 105

Pages: 175-184

Print publication date: 01/12/2014

Online publication date: 28/05/2015

Acceptance date: 19/05/2014

ISSN (print): 0165-1684

ISSN (electronic): 1872-7557

Publisher: Elsevier

URL: http://dx.doi.org/10.1016/j.sigpro.2014.05.022

DOI: 10.1016/j.sigpro.2014.05.022

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