On the Assumption of Spherical Symmetry and Sparseness for the Frequency-Domain Speech Model

A new independent component analysis (ICA) formulation called independent vector analysis (IVA) was proposed in order to solve the permutation problem in convolutive blind source separation (BSS). Instead of running ICA in each frequency bin separately and correcting the disorder with an additional...

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Bibliographic Details
Published in:IEEE transactions on audio, speech, and language processing speech, and language processing, 2007-07, Vol.15 (5), p.1521-1528
Main Authors: Lee, Intae, Lee, Te-Won
Format: Article
Language:English
Subjects:
Algorithms
Blind source separation
Blind source separation (BSS)
cocktail party problem
convolutive mixture
entropy estimator
frequency domain
Frequency domain analysis
Independent component analysis
independent component analysis (ICA)
independent vector analysis (IVA)
Instruction sets
Multidimensional systems
Natural language processing
order statistics
permutation problem
Probability density function
Probability density functions
RADICAL
Running
Separation
Signal analysis
Signal processing algorithms
Source separation
Speech
Speech analysis
statistical signal processing
Symmetry
Vector analysis
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Summary:A new independent component analysis (ICA) formulation called independent vector analysis (IVA) was proposed in order to solve the permutation problem in convolutive blind source separation (BSS). Instead of running ICA in each frequency bin separately and correcting the disorder with an additional algorithmic scheme afterwards, IVA exploited the dependency among the frequency components of a source and dealt with them as a multivariate source by modeling it with sparse and spherically, or radially, symmetric joint probability density functions (pdfs). In this paper, we compare the speech separation performances of IVA by using a group of l p -norm-invariant sparse pdfs where the value of and the sparseness can be controlled. Also, we derive an IVA algorithm from a nonparametric perspective with the constraint of spherical symmetry and high dimensionality. Simulation results confirm the efficiency of assuming sparseness and spherical symmetry for the speech model in the frequency domain.
ISSN:1558-7916
2329-9290
1558-7924
2329-9304
DOI:10.1109/TASL.2007.899231