Skip to main content
SpringerLink
Log in

Improved active noise control algorithm based on the convex combination method

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Noise can be harmful in certain situations, which leads to the need of controlling it. In general, a particular method of active noise control basically consists of creating a destructive overlap between noise and a reference signal (both with the same frequency and amplitude but with an opposite phase). Different algorithms can be used for this purpose, each of them having distinct features, like convergence, velocity, and steady state attenuation level. The convex combination method for active noise control (C-ANC) has been used successfully as it combines different characteristics of most algorithms found in literature. This method combines two distinct active noise control algorithms into a single one. For instance, it can use algorithms such as FXLMF, which has good attenuation velocity, and the FXLMS algorithm, which has a significant attenuation level. This work proposes a modified convex combination algorithm, named MC-FXLMS-F, in order to maximize the overall attenuation level. Modification of the convex combination method was implemented using the FXLMS-F algorithm. The new proposed algorithm was compared to others found in literature, such as the FXLMS, FXLMS-F, and C-FXLMS-F algorithms. The modified convex combination algorithm (MC-FXLMS-F) showed improvement in both convergence speed and attenuation level. It achieved the best performance among the most popular existing algorithms.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Hinamoto Y, Sakai H (2007) A filtered-X LMS algorithm for sinusoidal reference signals—effects of frequency mismatch. IEEE Signal Process Lett 14(4):259–262

    Article  Google Scholar 

  2. Kuo SM, Morgan DR (1999) Active noise control: a tutorial review. Proc IEEE 87(6):702–943

    Article  Google Scholar 

  3. Zhang M, Lan H, Ser W (2001) Cross-updated active noise control system with online secondary path modeling. IEEE Trans Speech Audio Process 9(5):598–602

    Article  Google Scholar 

  4. Ardekani IT, Abdulla WH (2010) Theoretical convergence analysis of FXLMS algorithm. Signal Process 90(12):3046–3055

    Article  Google Scholar 

  5. Gaur S, Gupta VK (2016) A review on filtered-x LMS algorithm. Int J Signal Process Syst 4(2):172–176

    Google Scholar 

  6. Cheer J, Elliott SJ (2015) Multichannel control systems for the attenuation of interior road noise in vehicles. Mech Syst Signal Process 60–61:753–769

    Article  Google Scholar 

  7. Kuo SM, Mitra S, Gan WS (2006) Active noise control system for headphone applications. IEEE Trans Control Syst Technol 14(2):331–335

    Article  Google Scholar 

  8. Kuo SM, Morgan DR (1996) Active noise control systems: algorithms and DSP implementations. Wiley, New York

    Google Scholar 

  9. Zhang L, Tao J, Qiu X (2012) Active control of transformer noise with an internally synthesized reference signal. J Sound Vib 331(15):3466–3475

    Article  Google Scholar 

  10. Zhao H, Zeng X, He Z, Li T (2013) Adaptive RSOV filter using the FELMS algorithm for nonlinear active noise control systems. Mech Syst Signal Process 34(1):378–392

    Article  Google Scholar 

  11. Leahy R, Zhou Z, Hsu YC (1995) Adaptive filtering of stable processes for active attenuation of impulsive noise. In: Proceedings of the 1995 international conference on acoustics, speech, and signal processing, vol 5, pp 2983–2986

  12. Wu L, He H, Qiu X (2011) An active impulsive noise control algorithm with logarithmic transformation. IEEE Trans Audio Speech Lang Process 19(4):1041–1044

    Article  Google Scholar 

  13. Bjarnason E (1995) Analysis of the filtered-X LMS algorithm. IEEE Trans Speech Audio Process 3:504–514

    Article  Google Scholar 

  14. Tobias OJ, Bermudez JCM, Bershad NJ, Seara R (1998) Mean weight behavior of the filtered-X LMS algorithm. In: IEEE International conference on acoustics, speech, and signal processing, pp 3545–3548

  15. Tobias OJ (1999) Stochastic analysis of the filtered-X LMS algorithm. Ph.D. dissertation Federal University, Santa Catarina, Brazil

  16. Reddy RM, Panahi IMS, Briggs R (2011) Hybrid FXRLS-FXNLMS adaptive algorithm for active noise control in fMRI application. IEEE Trans Control Syst Technol 19(2):474–480

    Article  Google Scholar 

  17. Sun G, Li M, Lim TC (2015) A family of threshold based robust adaptive algorithms for active impulsive noise control. Appl Acoust 97:30–36

    Article  Google Scholar 

  18. Akhtar MT, Abe M, Kawamata M (2006) A new variable step size LMS algorithm-based method for improved online secondary path modeling in active noise control systems. IEEE Trans Audio Speech Lang Process 14(2):720–726

    Article  Google Scholar 

  19. Chang DC, Chu FT (2013) A new variable tap-length and step-size FXLMS algorithm. IEEE Signal Process Lett 20(11):1122–1125

    Article  Google Scholar 

  20. Huang B, Xiao Y, Sun Y et al (2013) A variable step-size FXLMS algorithm for narrowband active noise control. IEEE Trans Audio Speech Lang Process 21(2):301–312

    Article  Google Scholar 

  21. Liu W, Pokharel PP, Principe JC (2007) Correntropy: properties, and applications in non-Gaussian signal processing. IEEE Trans Signal Process 55(11):5286–5298

    Article  MathSciNet  Google Scholar 

  22. Lu L, Zhao H, Chen B (2016) Improved-variable-forgetting-factor recursive algorithm based on the logarithmic cost for Volterra system identification. IEEE Trans Circuits Syst II 63(6):588–592

    Article  Google Scholar 

  23. George NV, Panda G (2012) A robust filtered-s LMS algorithm for nonlinear active noise control. Appl Acoust 73(8):836–841

    Article  Google Scholar 

  24. Zhou Y, Zhang Q, Yin Y (2015) Active control of impulsive noise with symmetric α-stable distribution based on an improved step-size normalized adaptive algorithm. Mech Syst Signal Process 56–57:320–339

    Article  Google Scholar 

  25. Tan L, Jiang J (2015) Active control of impulsive noise using a nonlinear companding function. Mech Syst Signal Process 58–59:29–40

    Article  Google Scholar 

  26. Oliveira LPR, Stallaert B, Janssens K, Auweraer HV, Sas P, Desmet W (2010) NEX-LMS: a novel adaptive control scheme for harmonic sound quality control. Mech Syst Signal Process 24(6):1727–1738

    Article  Google Scholar 

  27. Lu L, Zhao H, Chen C (2016) A normalized subband adaptive filter under minimum error entropy criterion. Signal Image Video Process 10(6):1097–1103

    Article  Google Scholar 

  28. Chang DC, Chu FT (2014) Feedforward active noise control with a new variable tap-length and step-size filtered-X LMS algorithm. IEEE Trans Audio Speech Lang Process 22(2):542–555

    Article  Google Scholar 

  29. Bouchard M (2003) Multichannel affine and fast affine projection algorithms for active noise control and acoustic equalization systems. IEEE Trans Speech Audio Process 11(1):54–60

    Article  Google Scholar 

  30. Ferrer M, Diego M, Gonzalez A et al (2013) Convex combination filtered-x algorithms for active noise control systems. IEEE Trans Audio Speech Lang Process 21(1):156–167

    Article  Google Scholar 

  31. Omour AMA, Zidouri A, Iqbal N et al (2016) Filtered-X least mean fourth (FXLMF) and leaky FXLMF adaptive algorithms. J Adv Signal Process 2016:1–20

    Google Scholar 

  32. Song P, Zhao H (2019) Filtered-x least mean square/fourth (FXLMS/F) algorithm for active noise control. Mech Syst Signal Process 120:69–82

    Article  Google Scholar 

  33. Zhao H, Zeng X, He Z et al (2016) Improved functional link artificial neural network via convex combination for nonlinear active noise control. Appl Soft Comput 42:351–359

    Article  Google Scholar 

  34. Akhtar MT, Mitsuhashi W (2009) Improving performance of FxLMS algorithm for active noise control of impulsive noise. J Sound Vib 327(3–5):647–656

    Article  Google Scholar 

  35. George NV, Gonzalez A (2014) Convex combination of nonlinear adaptive filters for active noise control. Appl Acoust 76:157–161

    Article  Google Scholar 

  36. Ferrer M, Diego M, Gonzalez A et al (2009) Convex combination of adaptive filters for ANC. In: 16th International Congress on sound and vibration, Cracow, Poland

Download references

Acknowledgements

This work was supported by the CNPq (National Council for Scientific and Technological Development) and the UFMG (Federal University of Minas Gerais).

Author information

Authors and Affiliations

  1. Departamento de Engenharia de Estruturas, UFMG, Av Antonio Carlos 6627, Belo Horizonte, 31270-901, Brazil

    Fernando Basílio Félix & Max de Castro Magalhães

  2. Departamento de Engenharia Mecânica, UFMG, Av Antonio Carlos 6627, Belo Horizonte, 31270-901, Brazil

    Guilherme de Souza Papini

Authors
  1. Fernando Basílio Félix
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Max de Castro Magalhães
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guilherme de Souza Papini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fernando Basílio Félix.

Additional information

Technical editor: José Roberto de França Arruda.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Félix, F.B., de Castro Magalhães, M. & de Souza Papini, G. Improved active noise control algorithm based on the convex combination method. J Braz. Soc. Mech. Sci. Eng. 43, 163 (2021). https://doi.org/10.1007/s40430-021-02866-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40430-021-02866-0

Keywords

Search

Navigation