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Hardware Reduction in Cascaded LMS Adaptive Filter for Noise Cancellation Using Feedback

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Abstract

The present work investigates the innovative concept of adaptive noise cancellation (ANC) using feedback connection of least-mean-square (LMS) adaptive filters for the sake of hardware reduction. The concept of cascading and feedback for real-time LMS-ANC are also described. The simulation model gives variation in the distinct signals of LMS-ANC like error signal, output signal and weights at various LMS filter parameters. An attempt has been made to provide solution in order to improve the performance of cascaded LMS adaptive noise canceller in terms of filter parameters. The results are obtained with the help of adaptive algorithm and feedback structure algorithm of LMS-ANC with different filter lengths and step sizes which provide high convergence speed of error signal. The signal-to-noise ratio for closed-loop LMS-ANC was found to be higher than single LMS-ANC system and equivalent to cascaded LMS-ANC. The novelty of the proposed model lies in reduction in hardware and low instantaneous power consumption making the model cost-effective as well as less complicated as compare to cascaded LMS-ANC.

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References

  1. N.I. Andrusenko’, T.N. Kachalina’, Y.G. Nikitenko, Comparative estimation of noise-stability characteristics in the presence of noise-like interference in specialized systems of information exchange. Radio Electron. Commun. Syst. 50, 81 (2007). https://doi.org/10.3103/s0735272707020069

    Article  Google Scholar 

  2. D. Bismor, LMS algorithm step size adjustment for fast convergence. Arch. Acoust. 37(1), 31–40 (2012)

    Article  Google Scholar 

  3. J. Brocker, U. Parlitz, M. Ogorzałek, Nonlinear noise reduction. Proc. IEEE 90(5), 898 (2002)

    Article  Google Scholar 

  4. J. Chhikara, J. Singh, Noise cancellation using adaptive algorithms. Int. J. Modern Eng. Res. 2(3), 792 (2012)

    Google Scholar 

  5. R.R. Chilipi, N. Al Sayari, R. Beig, K. Al Hosani, Multitasking control algorithm for grid-connected inverters in distributed generation applications using adaptive noise cancellation filters. IEEE Trans. Energy Convers. 31(2), 714 (2016)

    Article  Google Scholar 

  6. M. M. Dewasthale, R. D. Kharadkar, Improved NLMS algorithm with fixed step size and filter length using adaptive weight updation for acoustic noise cancellation, in Annual IEEE India Conference (INDICON), Pune, 1 (2014)

  7. S. Dixit, D. Nagaria, Design and analysis of cascaded LMS adaptive filters for noise cancellation. Circuits, Syst. Signal Process. 36(2), 742–766 (2017)

    Article  Google Scholar 

  8. S. Dixit, D. Nagaria, Neural network implementation of least-mean-square adaptive noise cancellation, in International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), Ghaziabad, 134 (2014)

  9. A.J. Eron, L.C. Han, Wide-area adaptive active noise cancellation. IEEE Trans. Circuit. Syst.-II: Analog Dig. Signal Process. 41(6), 405 (1994). https://doi.org/10.1109/82.300200

    Article  Google Scholar 

  10. W. Gao, J. Huang, J. Han, Multi-channel differencing adaptive noise cancellation with multi-kernel method. J. Syst. Eng. Electron. 26(3), 421 (2015). https://doi.org/10.1109/JSEE.2015.00049

    Article  Google Scholar 

  11. G.B. Giannakis, A.V. Dandawate, Higher-order statistics-based input/output system identification and application to noise cancellation. Circuits, Syst. Signal Process. 10(4), 485–511 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  12. S.S. Godbole, P.M. Palsodkar, V.P. Raut, FPGA implementation of adaptive LMS filter. IEEE Colloq. Int. Conf., India 2, 226 (2011)

    Google Scholar 

  13. D.-Y. Huang, S. Rahardja, The misadjustment of the cascaded LMS prediction filter, in IEEE International Symposium on Circuits and Systems, 2565 (2009)

  14. N. Kalyanasundaram, P. Palanisamy, Target detection by adaptive noise cancellation. Electron. Lett. (2008). https://doi.org/10.1049/el:20081432

    Google Scholar 

  15. A. Mandal, R. Mishra, Digital equalization for cancellation of noise-like interferences in adaptive spatial filtering. Circuits, Syst. Signal Process. 36(2), 675–702 (2017)

    Article  MATH  Google Scholar 

  16. A.K. Maurya. Cascade–Cascade Least Mean Square (LMS) Adaptive Noise Cancellation. Circuits, Syst. Signal Process. (2017), 1–42

  17. S. Mohapatra, A. Kar, An improved sub band sigmoid function based feedback Active Noise Cancellation, in 2015 Annual IEEE India Conference (INDICON), New Delhi, 1 (2015). https://doi.org/10.1109/indicon.2015.7443601

  18. M.H. Miry, A.H. Miry, H. K. Khleaf, Adaptive noise cancellation for speech employing fuzzy and neural network, in 1st International Conference on Energy, Power and Control (2010) 289

  19. C.R.C. Nakagawa, S. Nordholm, W.Y. Yan, Feedback cancellation with probe shaping compensation. IEEE Signal Process. Lett. 21(3), 365 (2014)

    Article  Google Scholar 

  20. T.O. Onur, R. Hacioglu, Adaptive echo and noise cancellation for car hands-free voice communication, in 21st Signal Processing and Communications Applications Conference, 1 (2013). https://doi.org/10.1109/siu.2013.6531188

  21. S. Panda, M. N. Mohanty, Performance analysis of LMS based algorithms used for impulsive noise cancellation, in 2016 International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, 1 (2016)

  22. G. Saxena, G. Subramaniam, D. Manohar, Real time implementation of adaptive noise Cancellation, in IEEE International Conference on Electro/Information Technology, (2008) pp. 431–436. https://doi.org/10.1109/eit.2008.4554341

  23. X. Sun, S.M. Kuo, Active narrowband noise control systems using cascading adaptive filter. IEEE Trans. Audio Speech Lang. Process. 15, 586–592 (2007)

    Article  Google Scholar 

  24. L. Tao, H. K. Kwan, A neural network method for adaptive noise cancellation, in IEEE International Symposium on Circuits and Systems, 567 (1999). https://doi.org/10.1109/iscas.1999.777635

  25. R.K. Thenua, S.K. Agarwal, Simulation and performance analysis of adaptive filter in noise cancellation. Int. J. Eng. Sci. Technol. 2(9), 43–73 (2010)

    Google Scholar 

  26. S.V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction (Wiley, New York, 2000)

    Google Scholar 

  27. H.S. Vu, K.H. Chan, A high-performance feedback FxLMS active noise cancellation VLSI circuit design for in-ear headphones. Circuits, Syst. Signal Process. 36(7), 2767–2785 (2017)

    Article  Google Scholar 

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Correspondence to Shubhra Dixit.

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Dixit, S., Nagaria, D. Hardware Reduction in Cascaded LMS Adaptive Filter for Noise Cancellation Using Feedback. Circuits Syst Signal Process 38, 930–945 (2019). https://doi.org/10.1007/s00034-018-0896-3

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  • DOI: https://doi.org/10.1007/s00034-018-0896-3

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