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A Novel Influence Function M-Estimator-Based for Active Noise Control

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
M-estimators are widely used in active noise control (ANC) systems in order to update the adaptive FIR filter taps. ANC systems reduce the noise level by generating anti-noise signals. Up to now, the evaluation of M-estimators capabilities has shown that there exists a need for further improvements in this area. In this paper, a new improved M-estimator is proposed. The sensitivity of the proposed algorithm to the variations of its constant parameter is checked in feedforward control. The effectiveness of the algorithm in both types is proved by comparing it with previous studies. Simulation results show the steady performance and fast initial convergence of the proposed algorithm.
Słowa kluczowe
Rocznik
Strony
499--506
Opis fizyczny
Bibliogr. 24 poz., rys., tab., wykr.
Twórcy
  • Department of Mechanical Engineering, University of Gonabad, Gonabad, 9691957678, Iran
  • Department of Electrical Engineering, Ferdowsi University of Mashhad, Mashhad, 9177948974, Iran
Bibliografia
  • 1. Akhtar M. T., Mitsuhashi W. (2010), A modified normalized FxLMS algorithm for active control of impulsive noise, Proceedings of 18th European Signal Processing Conference (EUSIPCO), IEEE, pp. 1-5, Aalborg.
  • 2. Ang L. Y. L., Koh Y. K., Lee H. P. (2017), The performance of active noise-canceling headphones in different noise environments, Applied Acoustics, 122: 16-22, doi: 10.1016/j.apacoust.2017.02.005.
  • 3. Behera S. K., Das D. P., Subudhi B. (2017), Adaptive nonlinear active noise control algorithm for active headrest with moving error microphones, Applied Acoustics, 123: 9-19, doi: 10.1016/j.apacoust.2017.03.002.
  • 4. Darvish M., Frank S., Paschereit C. O. (2015), Numerical and experimental study on the tonal noise generation of a radial fan, Journal of Turbomachinery, 137 (10): 101005, doi: 10.1115/1.4030498.
  • 5. Elliott S. (2001), Signal Processing for Active Control, Academic Press, Elsevier.
  • 6. Ertaş H., Kaçıranlar S., Güler H. (2017), Robust Liu-type estimator for regression based on M-estimator, Communications in Statistics-Simulation and Computation, 46 (5): 3907-3932, doi: 10.1080/03610918.2015.1045077.
  • 7. Khan W. U., Ye Z., Altaf F., Chaudhary N. I., Raja M. A. Z. (2019), A novel application of fireworks heuristic paradigms for reliable treatment of nonlinear active noise control, Applied Acoustics, 146: 246-260, doi: 10.1016/j.apacoust.2018.11.024.
  • 8. Kuo S. M., Morgan D. R. (1999), Active noise control: a tutorial review, Proceedings of the IEEE, 87 (6): 943-973, doi: 10.1109/5.763310.
  • 9. Lee J. W., Lee J. C., Pandey J., Ahn S. H., Kang Y. J. (2010), Mechanical properties and sound insulation effect of ABS/carbon-black composites, Journal of Composite Materials, 44 (14): 1701-1716, doi: 10.1177%2F0021998309357673.
  • 10. Li J., Chen W. (2018), Singular boundary method based on time-dependent fundamental solutions for active noise control, Numerical Methods for Partial Differential Equations, 34 (4): 1401-1421, doi: 10.1002/num.22263.
  • 11. Lu L., Zhao H. (2017), Active impulsive noise control using maximum correntropy with adaptive kernel size, Mechanical Systems and Signal Processing, 87 (part A) 180-191, doi: 10.1016/j.ymssp.2016.10.020.
  • 12. Nelson P. A., Elliott S. J. (1991), Active Control of Sound, Academic Press, Elsevier.
  • 13. Nunez I. J., Miranda J. G., Duarte M. V. (2019), Active noise control in acoustic shutters, Applied Acoustics, 152: 41-46, doi: 10.1016/j.apacoust.2019.03.024.
  • 14. Patel V., George N. V. (2015), Nonlinear active noise control using spline adaptive filters, Applied Acoustics, 93: 38-43, doi: 10.1016/j.apacoust.2015.01.009.
  • 15. Paul L. (1934), Process of silencing sound oscillations, Google patents.
  • 16. Sabet S. M., Keshavarz R., Ohadi A. (2018), Sound isolation properties of polycarbonate/clay and polycarbonate/silica nanocomposites, Iranian Polymer Journal, 27 (1): 57-66, doi: 10.1007/s13726-017-0585-2.
  • 17. Sabzevari S. A. H., Moavenian M. (2017), Application of reinforcement learning for active noise control, Turkish Journal of Electrical Engineering & Computer Sciences, 25 (4): 2606-2613.
  • 18. Sen K. M., Morgan D. R. (1996), Active Noise Control Systems: Algorithms and DSP Implementations, John Wiley and Sons.
  • 19. Suhail M., Chand S., Kibria B. G. (2019), Quantile-based robust ridge m-estimator for linear regression model in presence of multicollinearity and outliers, Communications in Statistics-Simulation and Computation, 1-13, doi: 10.1080/03610918.2019.1621339.
  • 20. Sun G., Li M., Lim T. C. (2015), Enhanced filtered-x least mean M-estimate algorithm for active impulsive noise control, Applied Acoustics, 90: 31-41, doi: 10.1016/j.apacoust.2014.10.012.
  • 21. Tan L., Jiang J. (2015), Active control of impulsive noise using a nonlinear companding function, Mechanical Systems and Signal Processing, 58: 29-40, doi: 10.1016/j.ymssp.2015.01.010.
  • 22. Thanigai P., Kuo S. M., Yenduri R. (2007), Nonlinear active noise control for infant incubators in neonatal intensive care units, Proceedings of 2007 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 1-109, Honolulu, doi: 10.1109/ICASSP.2007.366628.
  • 23. Vu H.-S., Chen K. H. (2017), A high-performance feedback FxLMS active noise cancellation VLSI circuit design for in-ear headphones, Circuits, Systems and Signal Processing, 36 (7): 2767-2785, doi: 10.1007/s00034-016-0436-y.
  • 24. Wu L., Qiu X. (2013), An M-estimator based algorithm for active impulse-like noise control, Applied Acoustics, 74 (3: 407-412, doi: 10.1016/j.apacoust.2012.06.019.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-75889c47-f372-4f5b-9e38-fc4c87aed136
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