Abstract
Complex independent component analysis (ICA) has found utility in separation of complex-valued signals such as communications, functional magnetic resonance imaging, and frequency-domain speeches. However, permutation ambiguity is a main problem of complex ICA for order-sensitive applications, e.g., frequency-domain speech separation. This paper proposes a semi-blind complex ICA algorithm based on negentropy maximization. The magnitude correlation of a source signal is utilized to constrain the separation process. As a result, the complex-valued signals are separated without permutation. Experiments with synthetic complex-valued signals, synthetic speech signals, and recorded speech signals are performed. The results demonstrate that the proposed algorithm can not only solve the permutation problem, but also achieve slightly improved separation compared to the standard blind algorithm.
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References
Waheed, K., Salem, F.M.: Blind Information-Theoretic Multiuser Detection Algorithms for DS-CDMA and WCDMA Downlink Systems. IEEE Transactions on Neural Networks 16(4), 937–948 (2005)
Calhoun, V.D., Adali, T.: Unmixing fMRI with Independent Component Analysis. IEEE Engineering in Medicine and Biology Magazine 25, 79–90 (2006)
Sawada, H., Mukai, R., Araki, S., Makino, S.: Frequency-domain Blind Source Separation, Speech Enhancement. Springer, New York (2005)
Cardoso, J.F., Souloumiac, A.: Blind Beamforming for Non Gaussian Signals. IEEE Proc. Radar Signal Proc. 140, 362–370 (1993)
Eriksson, J., Koivunen, V.: Complex-valued ICA Using Second Order Statistics. In: 14th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing, Sao Luis, Brazil, pp. 183–191 (2004)
Bingham, E., Hyvärinen, A.: A Fast Fixed-point Algorithm for Independent Component Analysis of Complex Valued Signals. Int. J. Neural Systems 10, 1–8 (2000)
Li, H., Adali, T.: A Class of Complex ICA Algorithms Based on Kurtosis Maximization. IEEE Transactions on Neural Networks 19, 408–420 (2008)
Novey, M., Adali, T.: Complex ICA by Negentropy Maximization. IEEE Transactions on Neural Networks 19, 596–609 (2008)
Lin, Q.-H., Liu, J., Zheng, Y.R., Liang, H., Calhoun, V.D.: Semiblind Spatial ICA of fMRI Using Spatial Constraints. Human Brain Mapping 31, 1076–1088 (2010)
Lu, W., Rajapakse, J.C.: Approach and Applications of Constrained ICA. IEEE Transactions on Neural Networks 16, 203–212 (2005)
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Lin, QH., Wang, LD., Lin, JG., Gong, XF. (2010). Constrained Complex-Valued ICA without Permutation Ambiguity Based on Negentropy Maximization. In: Vigneron, V., Zarzoso, V., Moreau, E., Gribonval, R., Vincent, E. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2010. Lecture Notes in Computer Science, vol 6365. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15995-4_34
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DOI: https://doi.org/10.1007/978-3-642-15995-4_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15994-7
Online ISBN: 978-3-642-15995-4
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