Abstract
Based on conventional natural gradient algorithm (NGA) and equivariant adaptive separation via independence algorithm (EASI), a novel sign algorithm for on-line blind separation of independent sources is presented. A sign operator for the adaptation of the separation model is obtained from the derivation of a generalized dynamic separation model. A variable step-size sign algorithm rooted in NGA is also derived to better match the dynamics of the input signals and unmixing matrix. The proposed algorithms are appealing in practice due to their computational simplicity. Experimental results verify the superior convergence performance over conventional NGA and EASI algorithm in both stationary and non-stationary environments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Cichocki, A., Amari, S.: Adaptive blind signal and image processing: learning algorithms and applications. John Wiley & Sons, Chichester (2002)
Chambers, J.A., Jafari, M.G., McLaughlin, S.: Variable step-size EASI algorithm for sequential blind source separation. Elect. Lett., 393–394 (2004)
Douglas, S.C., Cichocki, A.: On-line step-size selection for training of adaptive systems. IEEE Signal Processing Magazine (6), 45–46 (1997)
Georgiev, P., Cichocki, A., Amari, S.: On some extensions of the natural gradient algorithm. In: Proc. ICA, pp. 581–585 (2001)
Mathews, V.J., Xie, Z.: A stochastic gradient adaptive filter with gradient adaptive step size. IEEE Trans. Signal Process 41(6), 2075–2087 (1993)
Cardoso, J.-F., Laheld, B.H.: Equivariant adaptive source separation. IEEE Trans. Signal Process 44, 3017–3030 (1996)
Amari, S.: Natural Gradient Works Efficiently in Learning. Neural Computation 10, 251–276 (1998)
Amari, S., Douglas, S.C.: Why Natural Gradient. In: Proc. IEEE International Conference Acoustics, Speech, Signal Processing, Seattle, WA, May 1998, vol. II, pp. 1213–1216 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yuan, L., Sang, E., Wang, W., Chambers, J.A. (2005). An Effective Method to Improve Convergence for Sequential Blind Source Separation. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_22
Download citation
DOI: https://doi.org/10.1007/11539087_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
eBook Packages: Computer ScienceComputer Science (R0)