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Nonlinear Acoustic Echo Canceller to Combat Sigmoid-Type Nonlinearities Under Noisy Environment

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Abstract

This paper presents a nonlinear-acoustic-echo-cancellation (NAEC) technique to tackle sigmoid-type nonlinearities under noisy environment. The nonlinear echo in acoustic systems is inevitable due to the inherent nonlinear characteristics of amplifiers and/or loudspeakers, which deteriorates the quality of speech as well as audio signal reception. Here, the sigmoid-type nonlinearity is modelled by incorporating two control parameters, which determine the shaping- and clipping-parameter values of the saturation curve at a particular room temperature. These control parameters are adjusted by utilizing the variable-step-size (VSS) least-mean-square (LMS) algorithm to enhance the convergence rate and tracking capability of presented NAEC. Furthermore, the impulse response of a room (indoor channel) in the acoustic echo path is modelled as a tap-delay-line finite-impulse-response filter, whose tap-coefficients are estimated by utilizing a modified recursive-least-squares (RLS) algorithm (involving the noise statistics) at the different values of signal-to-noise-ratio (SNR), when correlated as well as uncorrelated input signals are processed. Simulation results demonstrate the efficiency and efficacy of above mentioned adaptive NAEC technique using the VSS-LMS and modified RLS algorithms in terms of the high convergence rate as well as high value of echo-return-loss-enhancement (ERLE) factor. Both the elevating value of shaping-parameter (i.e., increasing nonlinearity level) and the alleviating value of SNR adversely affect the performance of all NAECs. However, the VSS-LMS and modified RLS algorithm based presented adaptive NAEC outperforms the traditional VSS-LMS and normalized-least-mean-square (NLMS) algorithm based NAEC under similar conditions.

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References

  1. Nollett, B.S., & Jones D.L. (1997). Nonlinear echo cancellation for hands-free speakerphones. In Proc. IEEE Workshop Nonlinear Signal Image Process. (NSIP), Mackinac Island, MI (pp. 1–5).

  2. Fu, J., & Zhu, W.-P. (2008). A nonlinear acoustic echo canceller using sigmoid transform in conjunction with RLS algorithm. IEEE Transactions on Circuits and Systems II: Express Briefs, 55(10), 1056–1060.

    Article  Google Scholar 

  3. Ahgren, P. (2005). Acoustic echo cancellation and doubletalk detection using estimated loudspeaker impulse. IEEE Transactions on Speech and Audio Processing, 13(6), 1231–1237.

    Article  Google Scholar 

  4. Costa, J.-P., Lagrange, A., Arliaud, A. (2003). Acoustic echo cancellation using nonlinear cascade filters. In Proc. ICASSP, vol. 5, Hong Kong, China (pp. 389–392).

  5. Guerin, A., Faucon, G., & Bouquin-Jeannes, R. L. (2003). Nonlinear acoustic echo cancellation based on Volterra filters. IEEE Transactions on Speech and Audio Processing, 11(6), 672–683.

    Article  Google Scholar 

  6. Kuech, F., & Kellermann, W. (2004). Partitioned block frequency-domain adaptive second-order Volterra filter. IEEE Transactions on Signal Processing, 53(2), 564–575.

    Article  MathSciNet  MATH  Google Scholar 

  7. Kuech, F., Mitnacht, A., Kellermann, W. (2005). Nonlinear acoustic echo cancellation using adaptive orthogonalized power filters. In Proc. IEEE ICASSP, vol. 3, Philadelphia, PA (pp. 105–108).

  8. Panicker, T. M., & Mathews, V. J. (1998). Parallel-cascade realizations and approximations of truncated Volterra systems. IEEE Transactions on Signal Processing, 46(10), 2829–2832.

    Article  Google Scholar 

  9. Sentoni, G., & Altenberg, A. (2005). Nonlinear acoustic echo canceller with DABNET + FIR structure. In Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, New Paltz, NY, USA (pp. 37–40).

  10. Stenger, A., Trautmann, L., Rabenstein, R. (1999). Nonlinear acoustic echo cancellation with 2nd order adaptive Volterra filters. In Proc. IEEE ICASSP, vol. 2, Phoenix, USA (pp. 877–880).

  11. Stenger, A., & Rabenstein, R. (1999). Adaptation of acoustic echo cancellers incorporation a memoryless nonlinearity. In Proc. IEEE IWAENC, Pocono Manor, PA (pp. 168 – 171).

  12. Stenger, A., & Kellermann, W. (2000). Nonlinear acoustic echo cancellation with fast converging memoryless pre-processor. In Proc. International Conference on Acoustics, Speech, and Signal Processing. (ICASSP), vol. 2, Istanbul, Turkey (pp. II805–II808).

  13. Comminiello, D., Scarpiniti, M., Azpicueta-Ruiz, L. A., Garcia, J. A., & Uncini, A. (2013). Functional link adaptive filters for nonlinear acoustic echo cancellation. IEEE Transactions on Audio, Speech and Language Processing, 21(7), 1502–1512.

    Article  Google Scholar 

  14. Vaerenbergh, S.V., & Azpicueta-Ruiz, L.A. (2014). Kernel-based identification of Hammerstein systems for nonlinear acoustic echo cancellation. In Proc. IEEE ICASSP, vol. 1, Florence, Italy (pp. 3739–3743).

  15. Rai, A., & Kohli, A. K. (2015). Volterra filtering scheme using generalized variable step-size NLMS algorithm for nonlinear acoustic echo cancellation. Acta Acustica United with Acustica, 101(4), 821–828.

    Article  Google Scholar 

  16. Rai, A., & Kohli, A. K. (2014). Adaptive polynomial filtering using generalized variable step-size pth power (LMP) algorithm. Circuits, Systems and Signal Processing, 33(12), 3931–3947.

    Article  Google Scholar 

  17. Hamidia, M., & Amrouche A. (2019). Improving acoustic echo cancellation in hands-free communication systems. In Proc. ISPA, vol. 1, Mostaganem, Algeria (pp. 1–5).

  18. Dai, H., & Zhu, W.-P. (2006). Compensation of loudspeaker nonlinearity in acoustic echo cancellation using raised-cosine function. IEEE Transactions on Circuits and Systems II: Express Briefs, 53(11), 1190–1194.

    Article  Google Scholar 

  19. Breining, C., Dreiscitel, P., Hansler, E., Mader, A., Nitsch, B., Puder, H., et al. (1999). Acoustic echo control: an application of very-high-order adaptive filters. IEEE Signal Processing Magazine, 16(4), 42–69.

    Article  Google Scholar 

  20. Paleologu, C., Ciochina, S., & Benesty, J. (2008). Variable step-size NLMS algorithm for under-modeling acoustic echo cancellation. IEEE Signal Processing Letters, 15, 5–8.

    Article  Google Scholar 

  21. Kohli, A. K., & Mehra, D. K. (2006). Tracking of time-varying channels using two-step LMS-type adaptive algorithm. IEEE Transactions on Signal Processing, 54(7), 2606–2615.

    Article  MATH  Google Scholar 

  22. Garg, H. K., & Kohli, A. K. (2017). Excision of ocular artifacts from EEG using NVFF-RLS adaptive algorithm. Circuits, Systems and Signal Processing, 36(1), 404–419.

    Article  MATH  Google Scholar 

  23. Diniz, P. S. R. (2002). Adaptive filtering (2nd ed.). Norwell: Kluwer.

    MATH  Google Scholar 

  24. Lee, K., Baek, Y., & Park, Y. (2015). Nonlinear acoustic echo cancellation using a nonlinear postprocessor with a linearly constrained affine projection algorithm. IEEE Transactions on Circuits and Systems II: Express Briefs, 62(9), 881–885.

    Article  Google Scholar 

  25. Kuhn, E. V., Kolodziej, J. E., & Seara, R. (2014). Stochastic modeling of the NLMS algorithm for complex Gaussian input data and nonstationary environment. Digital Signal Processing, 30, 55–66.

    Article  MathSciNet  Google Scholar 

  26. Elko, G.W., Diethorn, E., Gansler, T. (2003). Room impulse response variation due to thermal fluctuation and its impact on acoustic echo cancellation. In Proc. IEEE IWAENC, Kyoto, Japan (pp. 67–70).

  27. Haykin, S. (1999). Neural networks (2nd ed.). Prentice-Hall: Pearson Education.

    MATH  Google Scholar 

  28. Aboulnasr, T., & Mayyas, K. (1997). A robust variable step-size LMS-type algorithm: analysis and simulations. IEEE Transactions on Signal Processing, 45(3), 631–639.

    Article  Google Scholar 

  29. Haykin, S. (1996). Adaptive filter theory (3rd ed.). Prentice-Hall: Englewood Cliffs.

    MATH  Google Scholar 

  30. Kwong, R. H., & Johnston, E. W. (1992). A variable step size LMS algorithm. IEEE Transactions on Signal Processing, 40(7), 1633–1642.

    Article  MATH  Google Scholar 

  31. Kohli, A. K., Rai, A., & Patel, M. K. (2011). Variable forgetting factor LS algorithm for polynomial channel model. ISRN Signal Processing, 915259, 1–4.

    Article  MATH  Google Scholar 

  32. Kohli, A. K., & Rai, A. (2013). Numeric variable forgetting factor RLS algorithm for second-order Volterra filtering. Circuits, System and Signal Processing, 32(1), 223–232.

    Article  MathSciNet  Google Scholar 

  33. Zhang, H., Tan, K., Wang, D. (2019). Deep learning for joint acoustic echo and noise cancellation with nonlinear distortions. In Proc. INTERSPEECH, vol. 1, Graz, Austria (pp. 15–19).

  34. Widrow, B., McCool, J. M., Larimore, M. G., & Johnson, C. R. (1976). Stationary and nonstationary learning characteristics of LMS adaptive filter. Proceedings of the IEEE, 64(8), 1151–1162.

    Article  MathSciNet  Google Scholar 

  35. Song, S., Lim, J. S., Baek, S. J., & Sung, K. M. (2002). Variable forgetting factor linear least squares algorithm for frequency selective fading channel estimation. IEEE Transactions on Vehicular Technology, 51(3), 613–616.

    Article  Google Scholar 

  36. Sunitha, T., & Malar, R. S. M. (2018). Nonlinear acoustic echo cancellation based on multichannel adaptive filters: a novel approach. Wireless Personal Communications, 102(4), 3269–3284.

    Article  Google Scholar 

  37. Papoulis, A. (1991). Probability random variables and stochastic processes (3rd ed.). New York: McGraw-Hill.

    MATH  Google Scholar 

  38. Kapoor, D. S., & Kohli, A. K. (2015). Simulation of basis expansion model for channel fading using AR1 process. Wireless Personal Communications, 85(3), 791–798.

    Article  Google Scholar 

  39. Singh, S., & Kohli, A. K. (2014). Wireless fading paradigm for antenna array receiver for a disk-type cluster of scatterers. Circuits, Systems and Signal Processing, 33(4), 1231–1244.

    Article  Google Scholar 

  40. Sukhumalwong, S., & Benjangkaprasert, C. (2006). Adaptive echo cancellation using variable step-size algorithm lattice filters. In Proceeding of IEEE TENCON Region 10 Conference, Hong Kong, China (pp. 1–4).

  41. Kapoor, D. S., & Kohli, A. K. (2018). Channel estimation and long-range prediction of fast fading channels for adaptive OFDM system. International Journal of Electronics, 105(9), 1451–1466.

    Article  Google Scholar 

  42. Halimeh, M. M., Huemmer, C., & Kellermann, W. (2019). A neural network-based nonlinear acoustic echo canceller. IEEE Signal Processing Letters, 26(12), 1827–1831.

    Article  Google Scholar 

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  1. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, 147004, India

    Amit Kumar Kohli & Jashu Sharma

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  1. Amit Kumar Kohli
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  2. Jashu Sharma
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Correspondence to Amit Kumar Kohli.

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Kohli, A.K., Sharma, J. Nonlinear Acoustic Echo Canceller to Combat Sigmoid-Type Nonlinearities Under Noisy Environment. Wireless Pers Commun 114, 3489–3506 (2020). https://doi.org/10.1007/s11277-020-07544-3

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  • DOI: https://doi.org/10.1007/s11277-020-07544-3

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