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Proceedings Paper

Independent vector analysis for real world speech processing
Author(s): Intae Lee; Te-Won Lee
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Paper Abstract

We introduce independent vector analysis (IVA) which is an extension of independent component analysis (ICA) to multivariate components. In a set of ICA mixtures, IVA groups dependent source components across different ICA mixtures and regard them as a multivariate source. This new formulation is an efficient framework for solving the permutation problem in frequency-domain blind source separation (BSS) and its application to n×n speech separation problem has been very successful. In this paper, we present a short tutorial on IVA and summarize the various models that have been proposed to model the frequency components of speech.

Paper Details

Date Published: 9 April 2007
PDF: 8 pages
Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 657602 (9 April 2007); doi: 10.1117/12.725192
Show Author Affiliations
Intae Lee, Univ. of California, San Diego (United States)
Te-Won Lee, Univ. of California, San Diego (United States)


Published in SPIE Proceedings Vol. 6576:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V
Harold H. Szu; Jack Agee, Editor(s)

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