Skip to main content
SpringerLink
Log in

Review on Active Noise Control Technology for α-Stable Distribution Impulsive Noise

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

Abstract

This paper provides a detailed overview of active impulsive noise control (AINC) technology of α-stable distribution noise. The second-order moment of the non-Gaussian impulsive noise does not exist, so the adaptive algorithms based on second-order statistics would lead to poor noise reduction performance. On account of this defect, a series of AINC algorithms have been proposed over the past 25 years. In this paper, we briefly classified the AINC algorithms into the classic algorithms and the expanded algorithms depending on different criteria, and both the classic AINC algorithms and the expanded AINC algorithms are further divided. Afterward, we introduced the principles and characteristics of different AINC algorithms and listed the weights update formulas of those algorithms. Finally, the computational complexity comparisons of common AINC algorithms and further recommendations are given, and a brief review about the experimental testing of AINC systems is provided in order to assist related scholars to carry out AINC researches more conveniently.

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

Similar content being viewed by others

Availability of data and material

Not applicable.

Code availability

Not applicable.

References

  1. K. Ahuja, J. Stevens, Recent advances in active noise control. AIAA J. 1, 1058–1067 (1990)

    Google Scholar 

  2. M.T. Akhtar, A. Nishihara, Data-reusing-based filtered-reference adaptive algorithms for active control of impulsive noise sources. Appl. Acoust. 92, 18–26 (2015)

    Article  Google Scholar 

  3. M.T. Akhtar, Binormalized data-reusing adaptive filtering algorithm for active control of impulsive sources. Digit. Signal Process. 49, 56–64 (2016)

    Article  Google Scholar 

  4. M.T. Akhtar, W. Mitsuhashi, Improved adaptive algorithm for active noise control of impulsive noise, in 2008 9th International Conference on Signal Processing, 26–29 Oct 2008 (2008), pp. 2669–2672

  5. M. Akhtar, A normalized filtered-x generalized fractional lower order moment adaptive algorithm for impulsive ANC systems (2018)

  6. M. Akhtar, W. Mitsuhashi, A modified normalized FxLMS algorithm for active control of impulsive noise, in European Signal Processing Conference (2010)

  7. M.T. Akhtar, M.A. Raja, Fractional processing-based active noise control algorithm for impulsive noise, in 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), 12–15 July 2015 (2015), pp. 10–14

  8. S. Ambike, J. Ilow, D. Hatzinakos, Detection for binary transmission in a mixture of Gaussian noise and impulsive noise modeled as an alpha-stable process. IEEE Signal Process. Lett. 1(3), 55–57 (1994)

    Article  Google Scholar 

  9. I.T. Ardekani, W.H. Abdulla, Theoretical convergence analysis of FxLMS algorithm. Signal Process. 90(12), 3046–3055 (2010)

    Article  MATH  Google Scholar 

  10. M. Aslam, P. Shi, C.C. Lim, Variable threshold-based selective updating algorithms in feed-forward active noise control systems. IEEE Trans. Circuits Syst. I Regul. Pap. 66(2), 782–795 (2018)

    Article  Google Scholar 

  11. E. Atmaca, I. Peker, A. Altin, Industrial noise and its effects on humans. Pol. J. Environ. Stud. 14(6), 721–726 (2005)

    Google Scholar 

  12. R.T. Bambang, Adjoint EKF learning in recurrent neural networks for nonlinear active noise control. Appl. Soft Comput. 8(4), 1498–1504 (2008)

    Article  Google Scholar 

  13. R.F. Barron, Industrial Noise Control and Acoustics (CRC Press, Boca Raton, 2002)

    Book  Google Scholar 

  14. M. Basner, W. Babisch, A. Davis, M. Brink, C. Clark, S. Janssen, S. Stansfeld, Auditory and non-auditory effects of noise on health. Lancet 383, 1325–1332 (2013)

    Article  Google Scholar 

  15. J.M. Bedout, M. Franchek, R.J. Bernhard, L. Mongeau, Adaptive-passive noise control with self-tuning Helmholtz resonators. J. Sound Vib. 202, 109–123 (1997)

    Article  Google Scholar 

  16. T. Bein, S. Herold, D. Mayer, Recent advances in active noise and vibration control, in 10th European Congress and Exposition on Noise Control Engineering, Euronoise 2015, June 1, 2015–June 3, 2015, Maastricht, Netherlands 2020. Euronoise (2015), pp. 2195–2200

  17. L.H. Bell, D.H. Bell, Industrial Noise Control: Fundamentals and Applications (Marcel Dekker, New York, 1994)

    Google Scholar 

  18. M. Bergamasco, L. Piroddi, Active noise control of impulsive noise using online estimation of an alpha-stable model, in 49th IEEE Conference on Decision and Control (CDC), 15–17 Dec 2010 (2010), pp. 36–41

  19. M. Bergamasco, F. Della Rossa, L. Piroddi, Active noise control with on-line estimation of non-Gaussian noise characteristics. J. Sound Vib. 331(1), 27–40 (2012)

    Article  Google Scholar 

  20. R.J. Bernhard, H.R. Hall, J.D. Jones, Adaptive-passive noise control. Inter-Noise 92, 427–430 (1992)

    Google Scholar 

  21. S. Bianchi, A. Corsini, A. Sheard, A critical review of passive noise control techniques in industrial fans. J. Eng. Gas Turbines Power 136(4), 044001 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  23. J. Burgess, Active adaptive sound control in a duct: a computer simulation. J. Acoust. Soc. Am. 70, 715–726 (1981)

    Article  Google Scholar 

  24. M. Calton, S. Sommerfeldt, Modeling systems of acoustic resonators for application in passive noise control. J. Acoust. Soc. Am. 139, 2205–2205 (2016)

    Article  Google Scholar 

  25. A. Carini, G.L. Sicuranza, Transient and steady-state analysis of filtered-x affine projection algorithms. IEEE Trans. Signal Process. 54(2), 665–678 (2006)

    Article  MATH  Google Scholar 

  26. D.P. Das, G. Panda, Active mitigation of nonlinear noise Processes using a novel filtered-s LMS algorithm. IEEE Trans. Speech Audio Process. 12(3), 313–322 (2004)

    Article  Google Scholar 

  27. D.P. Das, D.J. Moreau, B.S. Cazzolato, Adjoint nonlinear active noise control algorithm for virtual microphone. Mech. Syst. Signal Process. 27, 743–754 (2012)

    Article  Google Scholar 

  28. S.J. Elliott, P.A. Nelson, Active noise control. IEEE Signal Process. Mag. 10(4), 12–35 (1993)

    Article  Google Scholar 

  29. M. Ferrer, M. de-Diego, A. Gonzalez, G. Pinero, Convex combination of adaptive filters for ANC, in Proceedings of the 16th International Congress on Sound and Vibration (ICSV'09) (2009)

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

    Article  Google Scholar 

  31. N. George, G. Panda, Advances in active noise control: a survey, with emphasis on recent nonlinear techniques. Signal Process. 93, 363–377 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. P.G. Georgiou, P. Tsakalides, C. Kyriakakis, Alpha-stable modeling of noise and robust time-delay estimation in the presence of impulsive noise. IEEE Trans. Multimed. 1(3), 291–301 (1999)

    Article  Google Scholar 

  35. F. Gu, S. Chen, Z. Zhou, Y. Jiang, An enhanced normalized step-size algorithm based on adjustable nonlinear transformation function for active control of impulsive noise. Appl. Acoust. 176, 107853 (2021)

    Article  Google Scholar 

  36. H. Guo, Y.S. Wang, N.N. Liu, R.P. Yu, H. Chen, X.T. Liu, Active interior noise control for rail vehicle using a variable step-size median-LMS algorithm. Mech. Syst. Signal Process. 109, 15–26 (2018)

    Article  Google Scholar 

  37. X. Guo, J. Jiang, L. Tan, S. Du, Improved adaptive recursive even mirror Fourier nonlinear filter for nonlinear active noise control. Appl. Acoust. 146, 310–319 (2019)

    Article  Google Scholar 

  38. T. Habib, M. Kepesi, Open IEN issues of active noise control applications (2007)

  39. Z.C. He, H.H. Ye, E. Li, An efficient algorithm for nonlinear active noise control of impulsive noise. Appl. Acoust. 148, 366–374 (2019)

    Article  Google Scholar 

  40. J. Hong, J.C. Akers, R. Venugopal, L. Miin-Nan, A.G. Sparks, P.D. Washabaugh, D.S. Bernstein, Modeling, identification, and feedback control of noise in an acoustic duct. IEEE Trans. Control Syst. Technol. 4(3), 283–291 (1996)

    Article  Google Scholar 

  41. L. Hui, Z. Ming, S. Wee, A weight-constrained FxLMS algorithm for feedforward active noise control systems. IEEE Signal Process. Lett. 9(1), 1–4 (2002)

    Article  Google Scholar 

  42. Y. Ji, S. Chen, W. Zhu, The effect of pore numbers in the cell walls of soybean oil polyurethane foam on sound absorption performance. Appl. Acoust. 157, 107010 (2020)

    Article  Google Scholar 

  43. J. Jiang, Y. Li, Review of active noise control techniques with emphasis on sound quality enhancement. Appl. Acoust. 136, 139–148 (2018)

    Article  Google Scholar 

  44. Y. Jiang, S. Chen, F. Gu, H. Meng, Y. Cao, A modified feedforward hybrid active noise control system for vehicle. Appl. Acoust. 175, 107816 (2021)

    Article  Google Scholar 

  45. R.S. Job, Community response to noise: a review of factors influencing the relationship between noise exposure and reaction. J. Acoust. Soc. Am. 83, 991–1001 (1988)

    Article  Google Scholar 

  46. Y. Kajikawa, W. S. Gan, S. Kuo, Recent advances on Active noise control: open issues and innovative applications. APSIPA Trans. Signal Inf. Process. 1, E3 (2012)

    Article  Google Scholar 

  47. Y. Kajikawa, W. Gan, S.M. Kuo, Recent applications and challenges on active noise control, in 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), 4-6 Sept 2013 (2013), pp. 661–666

  48. B. Krstajic, Z. Zecevic, Z. Uskokovic, Increasing convergence speed of FxLMS algorithm in white noise environment. AEU-Int. J. Electron. C. 67, 848–853 (2013)

    Article  Google Scholar 

  49. S. Kuo, D. Morgan, Active noise control: a tutorial review. Proc. IEEE 87, 943–973 (1999)

    Article  Google Scholar 

  50. S. Kuo, D. Morgan, Active Noise Control Systems: Algorithms and DSP Implementations (John Wiley & Sons, Inc, New York, 1996)

    Google Scholar 

  51. P. Lara, F. Igreja, L.D.T.J. Tarrataca, D.B. Haddad, M.R. Petraglia, Exact expectation evaluation and design of variable step-size adaptive algorithms. IEEE Signal Process. Lett. 26(1), 74–78 (2018)

    Article  Google Scholar 

  52. R. Leahy, Z. Zhou, Y.C. Hsu, Adaptive filtering of stable processes for active attenuation of impulsive noise, vol 5 (1995)

  53. H.M. Lee, Z. Wang, K. Lim, H. Lee, A review of active noise control applications on noise barrier in three-dimensional/open space: myths and challenges. Fluct. Noise Lett. 18, 1930002 (2019)

    Article  Google Scholar 

  54. P. Li, X. Yu, Comparison study of active noise cancelation algorithms for impulsive noise (2011)

  55. C.E. Lin, C. Wen-Chih, H. An-Chih, W. Jon-Bi, A CFXLMS algorithm with selection detector for active noise control system, in 2005 IEEE International Conference on Industrial Technology, 14–17 Dec 2005 (2005), pp. 137–141

  56. L. Liu, S. Gujjula, P. Thanigai, S.M. Kuo, Still in womb: intrauterine acoustic embedded active noise control for infant incubators. Adv. Acoust. Vib. 2008, 495317 (2008)

    Google Scholar 

  57. L. Lu, H. Zhao, Adaptive Volterra filter with continuous lp-norm using a logarithmic cost for nonlinear active noise control. J. Sound Vib. 364, 14–29 (2016)

    Article  Google Scholar 

  58. L. Lu, H. Zhao, Active impulsive noise control using maximum correntropy with adaptive kernel size. Mech. Syst. Signal Process. 87, 180–191 (2017)

    Article  Google Scholar 

  59. P. Lueg, Verfahren zur Dämpfung von Schallschwingungen. Ger Pat 508 (1933)

  60. P. Leug, Process of silencing sound oscillations. U.S. Patent 2043416 (1936)

  61. S. Marburg, Developments in structural-acoustic optimization for passive noise control. Arch. Comput. Methods Eng. 9, 291–370 (2002)

    Article  MATH  Google Scholar 

  62. H. Meng, S. Chen, Particle swarm optimization based novel adaptive step-size FxLMS algorithm with reference signal smoothing processor for feedforward active noise control systems. Appl. Acoust. 174, 107796 (2021)

    Article  Google Scholar 

  63. H. Meng, S. Chen, A modified adaptive weight-constrained FxLMS algorithm for feedforward active noise control systems. Appl. Acoust. 164, 107227 (2020)

    Article  Google Scholar 

  64. D. Morgan, History, applications, and subsequent development of the FXLMS algorithm [DSP history]. IEEE Signal Process. Mag. 30, 172–176 (2013)

    Article  Google Scholar 

  65. W. Niu, C. Zou, B. Li, W. Wang, Adaptive vibration suppression of time-varying structures with enhanced FxLMS algorithm. Mech. Syst. Signal Process. 118, 93–107 (2019)

    Article  Google Scholar 

  66. A.M. Al Omour, A. Zidouri, N. Iqbal, A. Zerguine, Filtered-X least mean fourth (FXLMF) and leaky FXLMF adaptive algorithms. EURASIP J. Adv. Signal Process. 2016(1), 39 (2016)

    Article  Google Scholar 

  67. T. Padhi, M. Chandra, D.A. Kar, M.N.S. Swamy, A new adaptive control strategy for hybrid narrowband active noise control systems in a multi-noise environment. Appl. Acoust. 146, 355–367 (2018)

    Article  Google Scholar 

  68. T. Padhi, M. Chandra, Cascading time-frequency domain filtered-x LMS algorithm for active control of uncorrelated disturbances. Appl. Acoust. 149, 192–197 (2019)

    Article  Google Scholar 

  69. A. Panda, K. Das, A survey on actively controlling mixture of impulsive and Gaussian noise (2019)

  70. M. Pawelczyk, W. Wierzchowski, L. Wu, X. Qiu, An extension to the filtered-x LMS algorithm with logarithmic transformation, in 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 7–10 Dec 2015 (2015), pp. 454–459

  71. R.J. Peppin, Industrial noise and vibration control, by J. D. Irwin and E. R. Graf. J. Acoust. Soc. Am. 67(5), 1850–1851 (1980)

    Article  Google Scholar 

  72. F. Pfander, Danger of auditory impairment from impulse noise: a comparative study of the CHABA damage-risk criteria and those of the Federal Republic of Germany. J. Acoust. Soc. Am. 67, 628 (1980)

    Article  Google Scholar 

  73. G. Pinte, B. Stallaert, P. Sas, W. Desmet, J. Swevers, A novel design strategy for iterative learning and repetitive controllers of systems with a high modal density: theoretical background. Mech. Syst. Signal Process. 24(2), 432–443 (2010)

    Article  Google Scholar 

  74. R.M. Reddy, I.M.S. Panahi, R. Briggs, Hybrid FxRLS-FxNLMS adaptive algorithm for active noise control in fMRI application. IEEE Trans. Control Syst. Technol. 19(2), 474–480 (2011)

    Article  Google Scholar 

  75. M. Rupp, A.H. Sayed, Robust FxLMS algorithms with improved convergence performance. IEEE Trans. Speech Audio Process. 6(1), 78–85 (1998)

    Article  Google Scholar 

  76. F.H. Schmitz, Y.H. Yu, Helicopter impulsive noise: theoretical and experimental status, in Recent Advances in Aeroacoustics. ed. by A. Krothapalli, C.A. Smith (Springer, New York, 1986), pp. 149–243

    Chapter  Google Scholar 

  77. B.A. Schnaufer, W.K. Jenkins, New data-reusing LMS algorithms for improved convergence, in Proceedings of 27th Asilomar Conference on Signals, Systems and Computers, 1–3 Nov 1993, vol 1582 (1993), pp. 1584–1588

  78. M. Shao, C.L. Nikias, Signal processing with fractional lower order moments: stable processes and their applications. Proc. IEEE 81(7), 986–1010 (1993)

    Article  Google Scholar 

  79. G.L. Sicuranza, A. Carini, On the accuracy of generalized hammerstein models for nonlinear active noise control, in 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings, 24–27 April 2006 (2006), pp. 1411–1416

  80. A. Smith, A review of the non-auditory effects of noise on health. Work Stress. 5(1), 49–62 (1991)

    Article  Google Scholar 

  81. S.D. Snyder, N. Tanaka, Active control of vibration using a neural network. IEEE Trans. Neural Netw. 6(4), 819–828 (1995)

    Article  Google Scholar 

  82. S.A. Stansfeld, M.P. Matheson, Noise pollution: non-auditory effects on health. Br. Med. Bull. 68(1), 243–257 (2003)

    Article  Google Scholar 

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

    Article  Google Scholar 

  84. P. Song, H. Zhao, Filtered-x generalized mixed norm (FXGMN) algorithm for active noise control. Mech. Syst. Signal Process. 107, 93–104 (2018)

    Article  Google Scholar 

  85. B. Stallaert, G. Pinte, P. Sas, W. Desmet, J. Swevers, A novel design strategy for iterative learning and repetitive controllers of systems with a high modal density: application to active noise control. Mech. Syst. Signal Process. 24(2), 444–454 (2010)

    Article  Google Scholar 

  86. G. Sun, M. Li, T.C. Lim, Enhanced filtered-x least mean M-estimate algorithm for active impulsive noise control. Appl. Acoust. 90, 31–41 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  88. X. Sun, S.M. Kuo, G. Meng, Adaptive algorithm for active control of impulsive noise. J. Sound Vib. 291(1), 516–522 (2006)

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  Google Scholar 

  90. P. Thanigai, S.M. Kuo, R. Yenduri, Nonlinear active noise control for infant incubators in neo-natal intensive care units, in 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 15–20 April 2007 (20070, pp. I-109–I-112

  91. L. Thiery, C. Meyer-Bisch, Hearing loss due to partly impulsive industrial noise exposure at levels between 87 and 90 dB(A). J. Acoust. Soc. Am. 84, 651–659 (1988)

    Article  Google Scholar 

  92. H. Wang, H. Sun, Y. Sun, M. Wu, J. Yang, A narrowband active noise control system with a frequency estimation algorithm based on parallel adaptive notch filter. Signal Process. 154, 108–119 (2018)

    Article  Google Scholar 

  93. L. Wu, X. Qiu, Y. Guo, A simplified adaptive feedback active noise control system. Appl. Acoust. 81, 40–46 (2014)

    Article  Google Scholar 

  94. L. Wu, X. Qiu, Y. Guo, A generalized leaky FxLMS algorithm for tuning the waterbed effect of feedback active noise control systems. Mech. Syst. Signal Process. 106, 13–23 (2018)

    Article  Google Scholar 

  95. L. Wu, X. Qiu, Active impulsive noise control algorithm with post adaptive filter coefficient filtering. IET Signal Proc. 7(6), 515–521 (2013)

    Article  Google Scholar 

  96. L. Wu, X. Qiu, An M-estimator based algorithm for active impulse-like noise control. Appl. Acoust. 74(3), 407–412 (2013)

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  98. W. Xia, Y. Wang, A variable step-size diffusion LMS algorithm over networks with noisy links. Signal Process. 148, 205–213 (2018)

    Article  Google Scholar 

  99. L. Xiao, M. Wu, J. Yang, A new efficient filtered-x affine projection sign algorithm for active control of impulsive noise. Signal Process. 120, 456–461 (2016)

    Article  Google Scholar 

  100. F. Yang, Y. Cao, M. Wu, F. Albu, J. Yang, Frequency-domain filtered-x LMS algorithms for active noise control: a review and new insights. Appl. Sci. 8, 2313 (2018)

    Article  Google Scholar 

  101. G. Yang, J. Wang, G. Yue, S. Li, Complex baseband myriad filtering and maximum likelihood MSK demodulation under symmetric alpha-stable noise, in 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 15–18 May 2016 (2016), pp. 1–5

  102. K. Yin, H. Zhao, L. Lu, Functional link artificial neural network filter based on the q-gradient for nonlinear active noise control. J. Sound Vib. 435, 205–217 (2018)

    Article  Google Scholar 

  103. N. Yu, Z. Li, Y. Wu, R. Feng, B. Chen, Convex combination-based active impulse noise control system. J. Low Freq. Noise Vib. Act. Control 39(1), 190–202 (2019)

    Article  Google Scholar 

  104. H. Zayyani, Continuous mixed $p$-norm adaptive algorithm for system identification. IEEE Signal Process. Lett. 21(9), 1108–1110 (2014)

    Article  Google Scholar 

  105. A. Zeb, A. Mirza, Q.U. Khan, S.A. Sheikh, Improving performance of FxRLS algorithm for active noise control of impulsive noise. Appl. Acoust. 116, 364–374 (2017)

    Article  Google Scholar 

  106. J. Zhang, T.D. Abhayapala, Samarasinghe, P.N., Zhang, W., Jiang, S., Sparse complex FxLMS for active noise cancellation over spatial regions, in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 20–25 March 2016 (2016), pp. 524–528

  107. Q.Z. Zhang, W.S. Gan, Active noise control using a simplified fuzzy neural network. J. Sound Vib. 272(1), 437–449 (2004)

    Article  Google Scholar 

  108. S. Zhang, Y.S. Wang, H. Guo, C. Yang, X.L. Wang, N.N. Liu, A normalized frequency-domain block filtered-x LMS algorithm for active vehicle interior noise control. Mech. Syst. Signal Process. 120, 150–165 (2019)

    Article  Google Scholar 

  109. S. Zhang, W.X. Zheng, Recursive adaptive sparse exponential functional link neural network for nonlinear AEC in impulsive noise environment. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 4314–4323 (2018)

    Article  Google Scholar 

  110. H. Zhao, X. Zeng, Z. He, S. Yu, B. Chen, Improved functional link artificial neural network via convex combination for nonlinear active noise control. Appl. Soft Comput. 42, 351–359 (2016)

    Article  Google Scholar 

  111. T.L. Zhe, J. Jean, Filtered-X second-order Volterra adaptive algorithms. Electron. Lett. 33(8), 671–672 (1997)

    Article  Google Scholar 

  112. D. Zhou, V. DeBrunner, Efficient adaptive nonlinear filters for nonlinear active noise control. IEEE Trans. Circuits Syst. I Regul. Pap. 54(3), 669–681 (2007)

    Article  Google Scholar 

  113. Y. Zhou, Y. Yin, Q. Zhang, Active control of SαS impulsive noise based on a sigmoid transformation algorithm, vol 1 (2012)

  114. Y. Zhou, Q. Zhang, Y. Yin, 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 (2015)

    Article  Google Scholar 

  115. Y.L. Zhou, Y.X. Yin, Q.Z. Zhang, Active control of repetitive impulsive noise in a non-minimum phase system using an optimal iterative learning control algorithm. J. Sound Vib. 332(18), 4089–4102 (2013)

    Article  Google Scholar 

  116. T. Zhu, S. Chen, W. Zhu, Y. Wang, Optimization of sound absorption property for polyurethane foam using adaptive simulated annealing algorithm. J. Appl. Polym. Sci. 135, 46426 (2018)

    Article  Google Scholar 

  117. C. Zou, Adaptive vibration suppression of time-varying structures with enhanced FxLMS algorithm. Mech. Syst. Signal Process. 118, 93–107 (2018)

    Google Scholar 

Download references

Funding

This work was supported by the Jilin Provincial Natural Science Foundation Project (20180101070JC).

Author information

Authors and Affiliations

  1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun, 130022, China

    Shuming Chen, Feihong Gu, Chao Liang, Hao Meng, Kaiming Wu & Zhengdao Zhou

Authors
  1. Shuming Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feihong Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chao Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Kaiming Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhengdao Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SC was involved in data curation, writing-reviewing and editing, and project administration. FG was involved in conceptualization, methodology, and writing-original draft preparation. CL was involved in validation, visualization, and investigation. HM was involved in validation and resources. KW was involved in supervision and investigation. ZZ was involved in supervision and investigation.

Corresponding author

Correspondence to Shuming Chen.

Ethics declarations

Conflict of interest

No conflict of interest exists in the submission of this manuscript, and the manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described is original research and has not been published previously, in whole or in part. All the authors listed have approved the manuscript that is enclosed.

Additional information

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

Chen, S., Gu, F., Liang, C. et al. Review on Active Noise Control Technology for α-Stable Distribution Impulsive Noise. Circuits Syst Signal Process 41, 956–993 (2022). https://doi.org/10.1007/s00034-021-01814-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00034-021-01814-6

Keywords

Search

Navigation