Abstract
In this paper, we solve an ICA problem where both source and observation signals are multivariate, thus, vectorized signals. To derive the algorithm, we define dependence between vectors as Kullback-Leibler divergence between joint probability and the product of marginal probabilities, and propose a vector density model that has a variance dependency within a source vector. The example shows that the algorithm successfully recovers the sources and it does not cause any permutation ambiguities within the sources. Finally, we propose the frequency domain blind source separation (BSS) for convolutive mixtures as an application of IVA, which separates 6 speeches with 6 microphones in a reverberant room environment.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hyvärinen, A., Oja, E.: Independent Component Analysis. John Wiley and Sons, Chichester (2002)
Amari, S.I., Cichocki, A., Yang, H.H.: A new learning algorithm for blind signal separation. In: Adv. Neural information Processing Systems, vol. 8 (1996)
Barndorff-Nielsen, O.E.: Normal inverse gaussian distributions and stochastic volatility modeling. Scand. J. Statist. 24, 1–13 (1997)
Parra, L., Spence, C.: Convolutive blind separation of non-stationary sources. IEEE Trans. Speech Audio Processing 8, 320–327 (2000)
Ikram, M.Z., Morgan, D.R.: A beamforming approach to permutation alignment for multichannel frequency-domain blind speech separation. In: Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, pp. 881–884 (2002)
Murata, N., Ikeda, S., Ziehe, A.: An approach to blind source separation based on temporal structure of speech signals. Neurocomputing 41, 1–24 (2001)
Kim, T., Attias, H., Lee, S.Y., Lee, T.W.: Frequency domain blind source separation based on variance dependencies. In: Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, T., Eltoft, T., Lee, TW. (2006). Independent Vector Analysis: An Extension of ICA to Multivariate Components. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_21
Download citation
DOI: https://doi.org/10.1007/11679363_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
Online ISBN: 978-3-540-32631-1
eBook Packages: Computer ScienceComputer Science (R0)