Independent vector analysis for real world speech processing

Intae Lee; Te-Won Lee

doi:10.1117/12.725192

9 April 2007 Independent vector analysis for real world speech processing

Intae Lee, Te-Won Lee

Proceedings Volume 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V; 657602 (2007) https://doi.org/10.1117/12.725192
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

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.

Citation Download Citation

Intae Lee and Te-Won Lee "Independent vector analysis for real world speech processing", Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 657602 (9 April 2007); https://doi.org/10.1117/12.725192

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