As a method to tackle blind source separation (BSS) in the frequency domain, we introduce independent vector analysis (IVA), an extension of independent component analysis (ICA) from univariate components to multivariate components. Given a mixture of statistically independent multivariate sources where the mixing is constrained to be component-wise, ICA needs to be followed by an additional algorithmic scheme in order to correct the permutation disorder that occurs after the component-wise separation, whereas IVA utilizes the inner dependency of the multivariate components and separates the fellow source components together. The efficiency of this new formulation in solving the permutation problem has been proven in its application to convolutive mixture of independent speech signals. Maximum likelihood (ML) approaches or information theoretic approaches have been employed where the time–frequency model of speech has been modelled by several multivariate joint densities, and natural gradient or Newton method algorithms have been derived. Here, we present a gentle tutorial on IVA for the separation of speech signals in the frequency domain.
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Lee, I., Kim, T., Lee, TW. (2007). Independent Vector Analysis for Convolutive Blind Speech Separation. In: Makino, S., Sawada, H., Lee, TW. (eds) Blind Speech Separation. Signals and Communication Technology. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-6479-1_6
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