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A Complex Mixing Matrix Estimation Algorithm Based on Single Source Points

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Abstract

This paper considers the complex mixing matrix estimation in the under-determined blind source separation. An effective estimation algorithm through detecting single source points contributed by only one source is proposed. First, the single source points are detected by utilizing the real and the imaginary components of the time–frequency coefficients of mixed signals. The algorithm is suitable for the case in which the mixing matrix is complex, while traditional algorithms usually estimate the real mixing matrix. Then, through modeling and calculating, the mixing matrix of mixed signals can be estimated. Finally, the clustering process is improved in order to get more accurate results. The algorithm can estimate the complex mixing matrix when the number of sensors is less than that of sources. The experimental results validate the efficiency of the estimation algorithm.

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References

  1. F. Abrard, Y. Deville, A time–frequency blind signal separation method applicable to under-determined mixtures of dependent sources. Signal Process. 85(7), 1389–1403 (2005)

    Article  MATH  Google Scholar 

  2. S. Arberet, R. Gribonval, F. Bimbot, A robust method to count and locate audio sources in a stereophonic linear instantaneous mixture. In: 6th International Conference on Independent Component Analysis and Blind Signal Separation, Charleston, America (2006), pp. 536–543

  3. S. Arberet, R. Gribonval, F. Bimbot, A robust method to count and locate audio sources in a multichannel underdetermined mixture. IEEE Trans. Signal Process. 58(1), 121–133 (2010)

    Article  MathSciNet  Google Scholar 

  4. P. Bofill, M. Zibulevsky, Underdetermined blind source separation using sparse representation. Signal Process. 81(11), 2353–2362 (2001)

    Article  MATH  Google Scholar 

  5. F. Castell, J.J. Rieta, J. Millet et al., Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias. IEEE Tans. Biomed. Eng. 52(2), 258–267 (2005)

    Article  Google Scholar 

  6. M. Cobos, J.J. Lopez, Two-microphone separation of speech mixtures based on interclass variance maximization. J. Acoust. Soc. Am. 127(3), 1661–1672 (2010)

    Article  Google Scholar 

  7. T. Dong, Y. Lei, J. Yang, An algorithm for under-determined mixing matrix estimation. Neurocomputing 104(15), 26–34 (2013)

    Article  Google Scholar 

  8. P. Georgiev, F. Theis, A. Cichocki, Sparse component analysis and blind source separation of underdetermined mixtures. IEEE Trans. Neural Netw. 16(4), 992–996 (2005)

    Article  Google Scholar 

  9. Y. Guo, S. Huang, Y. Li, Single-mixture source separation using dimensionality reduction of ensemble empirical mode decomposition and independent component analysis. Circuits Syst. Signal Process. 1(6), 2047–2060 (2012)

    Article  MathSciNet  Google Scholar 

  10. K.T. Herring, A.V. Mueller, D.H. Staelin, Blind separation of noisy multivariate second-order statistics: remote sensing applications. IEEE Trans. Geosci. Remote Sens. 47(10), 3406–3415 (2009)

    Article  Google Scholar 

  11. J. Huang, H. Pan, S. Bi, Application of particle swarm optimization blind source separation technology in fault diagnosis of gearbox. J. Cent. South Univ. Technol. 15(2), 409–415 (2008)

    Article  Google Scholar 

  12. A. Hyvarinen, Testing the ICA mixing matrix based on inter-subject or inter-session consistency. NeuroImage 58(1), 122–136 (2011)

    Article  Google Scholar 

  13. S.G. Kim, C.D. Yoo, Under-determined blind source separation based on subspace representation. IEEE Trans. Signal Process. 57(7), 2604–2614 (2009)

    Article  MathSciNet  Google Scholar 

  14. K. Liu, L. Du, J. Wang, Underdetermined blind source separation based on single dominant source areas. Sci. China Ser. E Inf. Sci. 38(8), 1284–1301 (2008)

    MathSciNet  Google Scholar 

  15. G.R. Naik, D.K. Kumar, Determining number of independent sources in under-complete mixture. EURASIP J. Adv. Signal Process. Article ID 694850 (2009)

  16. G.R. Naik, D.K. Kumar, An overview of independent component analysis and its applications. Int. J. Comput. Inf. 35(1), 63–81 (2011)

    MATH  Google Scholar 

  17. G.R. Naik, D.K. Kumar, Dimensional reduction using blind source separation for identifying sources. Int. J. Innov. Comput. Inf. Control 7(2), 989–1000 (2011)

    Google Scholar 

  18. M. Puigt, Y. Deville, Time-frequency ratio-based blind separation methods for attenuated and time-delayed sources. Mech. Syst. Signal Process. 19(6), 1348–1379 (2005)

    Article  Google Scholar 

  19. G. Qian, L. Li, M. Luo, On the blind channel identifiability of MIMO–STBC systems using non-circular complex FastICA algorithm. Circuits Syst. Signal Process. 33(6), 1859–1881 (2014)

    Article  MathSciNet  Google Scholar 

  20. V.G. Reju, S.N. Koh, I.Y. Soon, An algorithm for mixing matrix estimation in instantaneous blind source separation. Signal Process. 89(9), 1762–1773 (2009)

    Article  MATH  Google Scholar 

  21. J.J. Thiagarajan, K.N. Ramamurthy, A. Spanias, Mixing matrix estimation using discriminative clustering for blind source separation. Digital Signal Process. 23(1), 9–18 (2013)

    Article  MathSciNet  Google Scholar 

  22. Y. Wang, Spatial Spectral Estimation Theory and Algorithm (Tsinghua University Press, Beijing, 2004)

    Google Scholar 

  23. M. Xiao, S. Xie, Y. Fu, Undetermined blind delayed source separation based on single source intervals in frequency domain. Acta Electr. Sin. 35(12), 2367–2373 (2007)

    Google Scholar 

  24. J. Xu, X. Yun, D. Hu et al., A fast mixing matrix estimation method in the wavelet domain. Signal Process. 95, 58–66 (2014)

    Article  Google Scholar 

  25. O. Yilmaz, S. Rickard, Blind separation of speech mixture via time–frequency masking. IEEE Trans. Signal Process. 52(7), 1830–1847 (2004)

    Article  MathSciNet  Google Scholar 

  26. X. Yu, T. Cao, D. Hu et al., Blind image separation based on wavelet transformation and sparse component analysis. J. Beijing Univ. Posts Telecommun. 33(2), 58–63 (2010)

    Google Scholar 

  27. X. Yu, J. Xu, D. Hu et al., A new blind image source separation algorithm based on feedback sparse component analysis. Signal Process. 93(1), 288–296 (2013)

    Article  Google Scholar 

  28. G. Zhou, Z. Yang, S. Xie et al., Mixing matrix estimation from sparse mixtures with unknown number of sources. IEEE Trans. Neural Netw. 22(2), 211–221 (2011)

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the Nation Nature Science Foundation of China (Nos. 61301095 and 51374099), the Fundamental Research Funds for the Central Universities of China (No. HEUCF140807), the Heilongjiang Province Natural Science Foundation (No. F201345) and the Heilongjiang Province Natural Science Foundation for the Youth (No. QC2012C070).

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Correspondence to Fang Ye.

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Li, Y., Nie, W. & Ye, F. A Complex Mixing Matrix Estimation Algorithm Based on Single Source Points. Circuits Syst Signal Process 34, 3709–3723 (2015). https://doi.org/10.1007/s00034-015-0027-3

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  • DOI: https://doi.org/10.1007/s00034-015-0027-3

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