Signal, Image and Video Processing

, Volume 13, Issue 3, pp 447–455 | Cite as

Reduced complexity diffusion filtered x least mean square algorithm for distributed active noise cancellation

  • Ruchi KukdeEmail author
  • M. Sabarimalai Manikandan
  • Ganapati Panda
Original Paper


A computationally efficient diffusion cooperation scheme-based distributed active noise control (DANC) system is proposed in this paper. It is observed that the conventional centralized multi-channel ANC (MANC) systems employed for noise reduction in a wide region are computationally complex and lack scalability. Additionally, the noise reduction for practically encountered noises is a challenging task, especially for multi-point environments. To overcome these drawbacks, in this paper, a diffusion filtered x least mean square (DFxLMS) algorithm is developed for DANC systems. The proposed DFxLMS-DANC scheme is modified using proximal secondary path bounds to reduce computational overhead. Also, the practical application of air-conditioner noise control is addressed in the presence of real primary and secondary path scenarios. It is shown that the total computational improvement in proposed DFxLMS-DANC and modified DFxLMS-DANC systems is 23.13% and 49.87%, respectively, over multiple error FxLMS-based MANC system. It is also demonstrated that the proposed method helps to achieve \(\sim \) 18 dB reduction in the air-conditioner noise levels in practical environments.


Active noise control (ANC) Filtered x least mean square algorithm Distributed noise cancellation Centralized multi-channel ANC Diffusion cooperation learning Secondary path effects 



  1. 1.
    Wax, M., Kailath, T.: Decentralized processing in sensor arrays. IEEE Trans. Acoust. Speech Signal Process. 33(5), 1123–1129 (1985)CrossRefGoogle Scholar
  2. 2.
    Lopes, C.G., Sayed, A.H.: Incremental adaptive strategies over distributed networks. IEEE Trans. Signal Process. 55(8), 4064–4077 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Lopes, C.G., Sayed, A.H.: Diffusion least-mean squares over adaptive networks: formulation and performance analysis. IEEE Trans. Signal Process. 56(7), 3122–3136 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Kuo, S.M., Morgan, D.R.: Active Noise Control Systems: Algorithms and DSP Implementations. Wiley, New York (1996)Google Scholar
  5. 5.
    Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice Hall, Upper Saddle River (1985)zbMATHGoogle Scholar
  6. 6.
    Narasimhan, S.V., Veena, S., Lokesha, H.: Variable step-size Griffiths’ algorithm for improved performance of feedforward/feedback active noise control. Signal Image Video Process. 4(3), 309–317 (2010)CrossRefzbMATHGoogle Scholar
  7. 7.
    Narasimhan, S.V., Veena, S.: New unbiased adaptive IIR filter: it’s robust and variable step-size versions and application to active noise control. Signal Image Video Process. 7(1), 197–207 (2013)CrossRefGoogle Scholar
  8. 8.
    Zheng-hong, D., Hui-gang, W., Guoyue, C.: Blind adaptive preprocessing to multichannel feedforward active noise control system. IET Signal Process. 7(6), 461–470 (2013)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Gao, M., Lu, J., Qiu, X.: A simplified subband ANC algorithm without secondary path modeling. IEEE/ACM Trans. Audio Speech Lang. Process. 24(7), 1164–1174 (2016)CrossRefGoogle Scholar
  10. 10.
    Ardekania, I.T., Abdullab, W.H.: Adaptive signal processing algorithms for creating spatial zones of quiet. Digit. Signal Process. 27, 129–139 (2014)CrossRefGoogle Scholar
  11. 11.
    Chang, C.Y., Li, S.T.: Active noise control in headsets by using a low-cost microcontroller. IEEE Trans. Ind. Electron. 58(5), 1936–1942 (2011)CrossRefGoogle Scholar
  12. 12.
    Pavithra, S., Narasimhan, S.V.: Feedback active noise control based on forward–backward LMS predictor. Signal Image Video Process. 7(6), 1083–1091 (2013)CrossRefGoogle Scholar
  13. 13.
    Pavithra, S., Narasimhan, S.V.: Feedback active noise control based on transform-domain forward–backward LMS predictor. Signal Image Video Process. 8(3), 479–487 (2014)CrossRefGoogle Scholar
  14. 14.
    Elliott, S., Stothers, I., Nelson, P.: A multiple error LMS algorithm and its application to the active control of sound and vibration. IEEE Trans. Acoust. Speech Signal Process. 35(10), 1423–1434 (1987)CrossRefGoogle Scholar
  15. 15.
    Sicuranza, G.L., Carini, A.: Filtered-x affine projection algorithm for multichannel active noise control using second-order Volterra filters. IEEE Signal Process. Lett. 11(11), 853–857 (2004)CrossRefGoogle Scholar
  16. 16.
    Lorente, J., Ferrer, M., de Diego, M., Gonzalez, A.: The frequency partitioned block modified filtered-x NLMS with orthogonal correction factors for multichannel active noise control. Digit. Signal Process. 43, 47–58 (2015)CrossRefGoogle Scholar
  17. 17.
    George, N.V., Panda, G.: A particle-swarm-optimization-based decentralized nonlinear active noise control system. IEEE Trans. Instrum. Meas. 61(12), 3378–3386 (2012)CrossRefGoogle Scholar
  18. 18.
    Gholami-Boroujeny, S., Eshghi, M.: Active noise control using an adaptive bacterial foraging optimization algorithm. Signal Image Video Process. 8(8), 1507–1516 (2014)CrossRefGoogle Scholar
  19. 19.
    Lopes, P.A.C., Gerald, J.A.B., Piedade, M.S.: The random walk model Kalman filter in multichannel active noise control. IEEE Signal Process. Lett. 22(12), 2244–2248 (2015)CrossRefGoogle Scholar
  20. 20.
    Contan, C., Kirei, B.S., Topa, M.D.: Error-dependent step-size control of adaptive normalized least-mean-square filters used for nonlinear acoustic echo cancellation. Signal Image Video Process. 10(3), 511–518 (2016)CrossRefGoogle Scholar
  21. 21.
    Ferrer, M., de Diego, M., Piero, G., Gonzalez, A.: Active noise control over adaptive distributed networks. Signal Process. 107, 82–95 (2015)CrossRefGoogle Scholar
  22. 22.
    Antoanzas, C., Ferrer, M., de Diego, M., Gonzalez, A.: Affine-projection-like algorithm for active noise control over distributed networks. In: IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), pp. 1–5 (2016)Google Scholar
  23. 23.
    Kukde, R., Panda, G., Manikandan, M.S.: On distributed non-linear active noise control using diffusion collaborative learning strategy. IET Signal Process. 12(4), 410–421 (2018)CrossRefGoogle Scholar
  24. 24.
    Antonanzas, C., Ferrer, M., Gonzalez, A., de Diego, M., Pinero, G.: Diffusion algorithm for active noise control in distributed networks. In: 22nd International Congress on Sound and Vibration (ICSV22) (2015)Google Scholar
  25. 25.
    Kukde, R., Manikandan, M.S., Panda, G.: Low complexity distributed active noise control using secondary path constraints. In: IEEE Region 10 Conference (TENCON), pp. 612–616 (2016)Google Scholar
  26. 26.
    George, N.V., Panda, G.: A reduced complexity adaptive Legendre neural network for nonlinear active noise control. In: 19th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 560–563 (2012)Google Scholar
  27. 27.
    Song, J.M., Park, P.: A diffusion strategy for the multichannel active noise control system in distributed network. In: International Conference on Computational Science and Computational Intelligence (CSCI), pp. 659–664 (2016)Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electrical SciencesIndian Institute of Technology BhubaneswarBhubaneswarIndia
  2. 2.C. V. Raman College of EngineeringBhubaneswarIndia

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