Skip to main content
Log in

Wavelet-Based ICA Using Maximum Likelihood Estimation and Information-Theoretic Measure for Acoustic Echo Cancellation During Double Talk Situation

  • Published:
Circuits, Systems, and Signal Processing Aims and scope Submit manuscript

Abstract

Acoustic echo cancellation (AEC) plays a prominent role in the present-day hands-free communication environment, owing to the usage of adaptive digital filter techniques. In a duplex communication scenario, there is a need for a double talk detection algorithm in the near-end speaker system which disables the update of the adaptive filter coefficients, thus hindering the process of echo cancellation and leading to a partial solution. To completely solve this problem, independent component analysis (ICA) is used to separate the far-end echo from the mixture of the near-end speech and the far-end echo signal. This paper proposes a new adaptive digital filter using maximum likelihood estimation of ICA and minimization of mutual information of ICA techniques for AEC. The advancement to this technique is made by transforming the observation into an adequate representation using wavelet decomposition. The performance of the echo cancellation is measured in terms of the echo return loss enhancement (ERLE). Higher ERLE indicates better echo cancellation. From the simulation results, it is found that the minimization of the mutual information of ICA has a higher value of ERLE than that by maximum likelihood estimation. The efficiency of the system thus increases by minimizing the processing time using wavelet ICA-based adaptive filter over the conventional adaptive filter.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. A.J. Bell, T.J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 7(6), 1004–1034 (1995)

    Article  Google Scholar 

  2. J. Benesty, D.R. Morgan, J.H. Cho, A new class of doubletalk detectors based on cross-correlation. IEEE Trans. Speech Audio Process. 8(3), 168–172 (2000)

    Google Scholar 

  3. H. Buchner, J. Benesty, T. Gansler, W. Kellermann, Robust extended multi delay filter and double-talk detector for acoustic echo cancellation. IEEE Trans. Audio Speech Lang. Process. 14(5), 1633–1644 (2006)

    Article  Google Scholar 

  4. C.S. Burrus et al., Introduction to Wavelet and Wavelet Transforms, 1st edn. (Prentice Hall Inc., Upper Saddle River, 1998)

    Google Scholar 

  5. J.F. Cardos, Infomax and maximum likelihood for source separation. IEEE Signal Process. Lett. 4(4), 112–114 (1997)

    Article  Google Scholar 

  6. J.F. Cardoso, Blind signal separation: statistical principles. Neural Comput. Surv. 85, 2009–2025 (1998)

    Google Scholar 

  7. J.F. Cardoso, Source separation using higher order moments. Proc. IEEE Int. Conf. Acoust. Speech Signal Process. 4, 2109–2112 (1984)

    Google Scholar 

  8. J.H. Cho, D.R. Morgan, J. Benesty, An objective technique for evaluating double talk detectors in acoustic echo canceller. IEEE Trans. Speech Audio Process. 7(11), 718–724 (1999)

    Article  Google Scholar 

  9. P. Common, Independent component analysis a new concept? Signal Process. 36, 287–314 (1994)

    Article  Google Scholar 

  10. P. Common, C. Jutten, Handbook of Blind Source Separation (Academics, New York, 2010)

    Google Scholar 

  11. D.L. Duttweiler, A twelve-channel digital echo canceller. IEEE Trans. Commun. 26, 647–653 (1978)

    Article  Google Scholar 

  12. T. Gansler, A double-talk detector based on coherence. IEEE Trans. Commun. 44(11), 1421–1427 (1996)

    Article  Google Scholar 

  13. T. Gansler, J. Benesty, A frequency-domain double-talk detector based on a normalized cross-correlation vector. Signal Process. 81, 1783–1787 (2001)

    Article  Google Scholar 

  14. L. Hongyan, R. Guanglong, Blind separation of noisy mixed speech signals based independent component analysis, in Pervasive Computing Signal Processing and Applications (PCSPA). 2010 First International Conference on Pervasive Computing, Signal Processing and Applications (2010), pp. 586–589

  15. A. Hyvärinen, Fast and robust fixed-point algorithm for independent component analysis. IEEE Trans. Neural Netw. 10(3), 624–634 (1999)

    Article  Google Scholar 

  16. A. Hyvärinen, J. Karhunen, E. Oja, Independent Component Analysis (Wiley, New York, 2001)

    Book  Google Scholar 

  17. G. Jake, Learning echo paths during continuous double-talk using semi-blind source separation. IEEE Trans. Audio Speech Lang. Process. 20(2), 646–660 (2012)

    Article  Google Scholar 

  18. J.C. Jenq, S.F. Hsieh, A double talk resistant echo cancellation based on the iterative maximal-length correlation. Proc. Int. Symp. Circuits Syst. 5, 237–240 (2000)

  19. K.H. Lee, J.H. Chang, N.S. Kim, S. Kang, Y. Kim, Frequency domain double-talk detection based on the Gaussian mixture model. IEEE Signal Process. 17(5), 453–456 (2010)

    Article  Google Scholar 

  20. M. Luo, L. Li, G. Qian, H. Liao, Multidimensional blind separation using higher-order statistics: application non-cooperative STBC systems. Circuits Syst. Signal Process. 33(7), 2173–2192 (2014)

    Article  MathSciNet  Google Scholar 

  21. U. Mahbub, S.A. Fattah, A single-channel acoustic echo cancellation scheme using gradient-based adaptive filtering. Circuits Syst. Signal Process. 33(5), 1541–1572 (2014)

    Article  Google Scholar 

  22. T. Mei, F. Yin, J. Wang, Blind source separation based on cumulants with time and frequency non-properties. IEEE Trans. Audio Speech Lang. Process. 17(8), 1099–1108 (2009)

    Article  Google Scholar 

  23. G.R. Naik, D.K. Kumar, Improving the quality of the audio sources using Gaussianity reduction technique. Int. J. Electron. 98(7), 949–959 (2011)

    Article  Google Scholar 

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

    MATH  Google Scholar 

  25. G.R. Naik, W. Wang, Audio analysis of statistically instantaneous signals with mixed Gaussian probability distributions. Int. J. Electron. 99(10), 1333–1350 (2012)

    Article  Google Scholar 

  26. G.R. Naik, Measure of quality of source separation for sub-Gaussian and super-Gaussian audio mixtures. Inform. Int. J. Comput. Inform. 23(4), 581–599 (2012)

    MATH  MathSciNet  Google Scholar 

  27. F. Nesta, T.S. Wada, B.-H. Juang, Batch-online semi-blind source separation applied to multi-channel acoustic echo cancellation. IEEE Trans. Audio Speech Lang. Process. 19(3), 583–599 (2011)

    Article  Google Scholar 

  28. Y.-J. Park, H.M. Park, DTD-free nonlinear acoustic echo cancellation based on independent component analysis. Electron. Lett. 46(12), 1585–1586 (2010)

    Article  Google Scholar 

  29. D.T. Pham, P. Garat, C. Jutten, Separation of a mixture of independent sources through a maximum likelihood approach, in Proceedings of the EUSIPCO (1992), pp. 771–774

  30. D.W.E. Schobben, P.W. Somment, A frequency domain blind signal separation method based on decorrelation. IEEE Trans. Audio Signal Process. 50(8), 1855–1864 (2002)

    Article  Google Scholar 

  31. M.M. Sondhi, An adaptive echo canceller. Bell Syst. Tech. J. 46(3), 497–510 (1967)

    Article  Google Scholar 

  32. I.Y. Soon, S.N. Koh, C.K. Yeo, Wavelet for speech denoising, in Proceedings of the IEEE TENCON (1997), pp. 479–482

  33. R. Takeda et al., Efficient blind dereverberation and echo cancellation based on independent component analysis for actual acoustic signals. Neural Comput. 24(1), 234–272 (2012)

    Article  MATH  MathSciNet  Google Scholar 

  34. J.Y. Tourneret, N.B. Bershad, C. Bermudez, Echo cancellation—the generalized likelihood ratio test for double-talk versus channel change. IEEE Trans. Signal Process. 57(3), 916–926 (2009)

    Article  MathSciNet  Google Scholar 

  35. T.S. Wada, B.H. Juang, Enhancement of residual echo for robust acoustic echo cancellation. IEEE Trans. Audio Speech Lang. Process. 20(1), 175–180 (2012)

    Article  Google Scholar 

  36. T.S. Wada, B.H. Juang, Towards robust acoustic echo cancellation during double-talk and near-end background noise via enhancement of residual echo, in Proceedings of the IEEE ICASSP (2008), pp. 253–256

  37. B. Widrow, M.E. Hoff, Adaptive switching circuits. IRE Wescon Conv. Rec. 4, 96–104 (1960)

  38. H. Ye, B.-X. Wu, A new double talk detection algorithm based on the orthogonality theorem. IEEE Trans. Commun. 39(11), 1542–1545 (1991)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Editor-in-chief, Prof. M.N.S. Swamy, for his help in improving the presentation of the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Mohanaprasad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohanaprasad, K., Arulmozhivarman, P. Wavelet-Based ICA Using Maximum Likelihood Estimation and Information-Theoretic Measure for Acoustic Echo Cancellation During Double Talk Situation. Circuits Syst Signal Process 34, 3915–3931 (2015). https://doi.org/10.1007/s00034-015-0038-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-015-0038-0

Keywords

Navigation