Author Type

Graduate Student

Date of Award

2010

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Abstract

Blind source separation (BSS) refers to a class of methods by which multiple sensor signals are combined with the aim of estimating the original source signals. Independent component analysis (ICA) is one such method that effectively resolves static linear combinations of independent non-Gaussian distributions. We propose a method that can track variations in the mixing system by seeking a compromise between adaptive and block methods by using mini-batches. The resulting permutation indeterminacy is resolved based on the correlation continuity principle. Methods employing higher order cumulants in the separation criterion are susceptible to outliers in the finite sample case. We propose a robust method based on low-order non-integer moments by exploiting the Laplacian model of speech signals. We study separation methods for even (over)-determined linear convolutive mixtures in the frequency domain based on joint diagonalization of matrices employing time-varying second order statistics. We investigate the sources affecting the sensitivity of the solution under the finite sample case such as the set size, overlap amount and cross-spectrum estimation methods.

Extent

xi, 112 p. : ill. (some col.)

Comments

by Savaskan Bulek.|Thesis (Ph.D.)--Florida Atlantic University, 2010.|Includes bibliography.|Electronic reproduction. Boca Raton, Fla., 2010. Mode of access: World Wide Web.|by Savaskan Bulek.

Identifier

FADT2953201

Format

pdf

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