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Neural Computing and Applications

, Volume 31, Issue 12, pp 9221–9240 | Cite as

A novel application of kernel adaptive filtering algorithms for attenuation of noise interferences

  • Muhammad Asif Zahoor Raja
  • Naveed Ishtiaq Chaudhary
  • Zaheer Ahmed
  • Ata Ur Rehman
  • Muhammad Saeed AslamEmail author
Original Article
  • 39 Downloads

Abstract

In this study, adaptive filtering paradigm-based kernel least mean square (KLMS) algorithm is developed for feed-forwarded active noise control (ANC) systems by exploiting the strength of activation functions of neural network (NN) as kernels. The transfer functions NN based on logistic, tan-sigmoid and inverse-tan kernels are introduced as a variant of KLMS, normalized KLMS and affine projection KLMS algorithms. All three proposed adaptive filtering strategies are implemented for optimization of design parameters of ANC system of a headset with nonlinear noise interference under several scenarios based on tonal, narrowband, broadband and varying acoustic path. Comparison studies on the basis of detailed numerical experimentation are conducted to establish the worth of the proposed methodologies.

Keywords

Adaptive algorithms Active noise control Kernal LMS Activation functions 

Notes

Compliance with ethical standards

Conflict of interest

All authors declared that there are no potential conflicts of interest.

Human and animal rights statements

All authors declared that there is no research involving human and/or animal.

Informed consent

All authors declared that there is no material that required informed consent.

References

  1. 1.
    Harris CM (1991) Handbook of acoustical measurements and noise control. McGraw-Hill, New York, pp 30–45Google Scholar
  2. 2.
    Boll S (1979) Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans Acoust Speech Signal Process 27(2):113–120CrossRefGoogle Scholar
  3. 3.
    Hänsler E, Schmidt G (2005) Acoustic echo and noise control: a practical approach, vol 40. Wiley, New YorkGoogle Scholar
  4. 4.
    Kuo SM, Morgan D (1995) Active noise control systems: algorithms and DSP implementations. Wiley, New YorkGoogle Scholar
  5. 5.
    Elliott SJ, Nelson PA (1993) Active noise control. IEEE Signal Process Mag 10(4):12–35CrossRefGoogle Scholar
  6. 6.
    Kuo SM, Morgan DR (1999) Active noise control: a tutorial review. Proc IEEE 87(6):943–973CrossRefGoogle Scholar
  7. 7.
    George NV, Panda G (2013) Advances in active noise control: a survey, with emphasis on recent nonlinear techniques. Sig Process 93(2):363–377CrossRefGoogle Scholar
  8. 8.
    Douglas SC (1999) Fast implementations of the filtered-X LMS and LMS algorithms for multichannel active noise control. IEEE Trans Speech Audio Process 7(4):454–465CrossRefGoogle Scholar
  9. 9.
    Bjarnason E (1995) Analysis of the filtered-X LMS algorithm. IEEE Trans Speech Audio Process 3(6):504–514CrossRefGoogle Scholar
  10. 10.
    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):720–726CrossRefGoogle Scholar
  11. 11.
    Aslam MS, Raja MAZ (2015) A new adaptive strategy to improve online secondary path modeling in active noise control systems using fractional signal processing approach. Signal Process 107:433–443CrossRefGoogle Scholar
  12. 12.
    Shah SM, Samar R, Raja MAZ, Chambers JA (2014) Fractional normalised filtered-error least mean squares algorithm for application in active noise control systems. Electron Lett 50(14):973–975CrossRefGoogle Scholar
  13. 13.
    Shah SM, Samar R, Khan NM, Raja MAZ (2016) Fractional-order adaptive signal processing strategies for active noise control systems. Nonlinear Dyn 85(3):1363–1376CrossRefMathSciNetGoogle Scholar
  14. 14.
    Zhang S, Wang YS, Guo H, Yang C, Wang XL, Liu NN (2019) A normalized frequency-domain block filtered-x LMS algorithm for active vehicle interior noise control. Mech Syst Signal Process 120:150–165CrossRefGoogle Scholar
  15. 15.
    Feng T, Sun G, Li M, Lim TC (2017) Channel self-adjusting filtered-x LMS algorithm for active control of vehicle road noise. Int J Veh Noise Vib 13(3–4):267–281CrossRefGoogle Scholar
  16. 16.
    Chang CY, Chen DR (2010) Active noise cancellation without secondary path identification by using an adaptive genetic algorithm. IEEE Trans Instrum Meas 59(9):2315–2327CrossRefGoogle Scholar
  17. 17.
    Rout NK, Das DP, Panda G (2016) Particle swarm optimization based nonlinear active noise control under saturation nonlinearity. Appl Soft Comput 41:275–289CrossRefGoogle Scholar
  18. 18.
    George NV, Panda G (2012) A particle-swarm-optimization-based decentralized nonlinear active noise control system. IEEE Trans Instrum Meas 61(12):3378–3386CrossRefGoogle Scholar
  19. 19.
    Khan WU, Ye Z, Chaudhary NI, Raja MAZ (2018) Backtracking search integrated with sequential quadratic programming for nonlinear active noise control systems. Appl Soft Comput 73:666–683CrossRefGoogle Scholar
  20. 20.
    Raja MAZ, Aslam MS, Chaudhary NI, Khan WU (2018) Bio-inspired heuristics hybrid with interior-point method for active noise control systems without identification of secondary path. Front Inf Technol Electron Eng 19(2):246–259CrossRefGoogle Scholar
  21. 21.
    Raja MAZ, Aslam MS, Chaudhary NI, Nawaz M, Shah SM (2017) Design of hybrid nature-inspired heuristics with application to active noise control systems. Neural Comput Appl.  https://doi.org/10.1007/s00521-017-3214-2 CrossRefGoogle Scholar
  22. 22.
    Momani Z, Al Shridah M, Arqub OA, Al-Momani M, Momani S (2018) Modeling and analyzing neural networks using reproducing kernel Hilbert space algorithm. Appl Math 12(1):89–99MathSciNetGoogle Scholar
  23. 23.
    Arqub OA, Maayah B (2018) Solutions of Bagley-Torvik and Painlevé equations of fractional order using iterative reproducing kernel algorithm with error estimates. Neural Comput Appl 29(5):1465–1479CrossRefGoogle Scholar
  24. 24.
    Arqub OA (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations. Neural Comput Appl 28(7):1591–1610CrossRefGoogle Scholar
  25. 25.
    Arqub OA, Rashaideh H (2018) The RKHS method for numerical treatment for integrodifferential algebraic systems of temporal two-point BVPs. Neural Comput Appl 30(8):2595–2606CrossRefGoogle Scholar
  26. 26.
    Emamjome M, Azarnavid B, Ghehsareh HR (2017) A reproducing kernel Hilbert space pseudospectral method for numerical investigation of a two-dimensional capillary formation model in tumor angiogenesis problem. Neural Comput Appl.  https://doi.org/10.1007/s00521-017-3184-4 CrossRefGoogle Scholar
  27. 27.
    Liu W, Pokharel PP, Principe JC (2008) The kernel least-mean-square algorithm. IEEE Trans Signal Process 56(2):543–554CrossRefMathSciNetzbMATHGoogle Scholar
  28. 28.
    Chen B, Zhao S, Zhu P, Príncipe JC (2012) Mean square convergence analysis for kernel least mean square algorithm. Signal Process 92(11):2624–2632CrossRefGoogle Scholar
  29. 29.
    Liu W, Principe JC, Haykin S (2011) Kernel adaptive filtering: a comprehensive introduction, vol 57. Wiley, New YorkGoogle Scholar
  30. 30.
    Tobar FA, Kung SY, Mandic DP (2014) Multikernel least mean square algorithm. IEEE Trans Neural Netw Learn Syst 25(2):265–277CrossRefGoogle Scholar
  31. 31.
    Gil-Cacho JM, Signoretto M, van Waterschoot T, Moonen M, Jensen SH (2013) Nonlinear acoustic echo cancellation based on a sliding-window leaky kernel affine projection algorithm. IEEE Trans Audio Speech Lang Process 21(9):1867–1878CrossRefGoogle Scholar
  32. 32.
    Mitra R, Bhatia V (2016) Adaptive sparse dictionary-based kernel minimum symbol error rate post-distortion for nonlinear LEDs in visible light communications. IEEE Photonics J 8(4):1–13CrossRefGoogle Scholar
  33. 33.
    Lu L, Zhao H, Chen B (2017) Time series prediction using kernel adaptive filter with least mean absolute third loss function. Nonlinear Dyn 90(2):999–1013CrossRefMathSciNetzbMATHGoogle Scholar
  34. 34.
    Yazdi HS, Pakdaman M, Modaghegh H (2011) Unsupervised kernel least mean square algorithm for solving ordinary differential equations. Neurocomputing 74(12–13):2062–2071CrossRefGoogle Scholar
  35. 35.
    Chaudhary NI, Raja MAZ, Khan JA, Aslam MS (2013) Identification of input nonlinear control autoregressive systems using fractional signal processing approach. Sci World J.  https://doi.org/10.1155/2013/467276 CrossRefGoogle Scholar
  36. 36.
    Chaudhary NI, Raja MAZ (2015) Identification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithms. Nonlinear Dyn 79(2):1385–1397CrossRefMathSciNetzbMATHGoogle Scholar
  37. 37.
    Chaudhary NI, Raja MAZ (2015) Design of fractional adaptive strategy for input nonlinear Box–Jenkins systems. Signal Process 116:141–151CrossRefGoogle Scholar
  38. 38.
    Zhang S, Tan W, Wang Q, Wang N (2018) A new method of online extreme learning machine based on hybrid kernel function. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3629-4 CrossRefGoogle Scholar
  39. 39.
    Zhao M, Tian Z, Chow TW (2018) Fault diagnosis on wireless sensor network using the neighborhood kernel density estimation. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3342-3 CrossRefGoogle Scholar
  40. 40.
    Faris H, Hassonah MA, Ala’M AZ, Mirjalili S, Aljarah I (2018) A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture. Neural Comput Appl 30(8):2355–2369CrossRefGoogle Scholar
  41. 41.
    Xie X, Li B, Chai X (2017) A manifold framework of multiple-kernel learning for hyperspectral image classification. Neural Comput Appl 28(11):3429–3439CrossRefGoogle Scholar
  42. 42.
    Sodhro AH, Malokani AS, Sodhro GH, Muzammal M, Zongwei L (2019) An adaptive QoS computation for medical data processing in intelligent healthcare applications. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3931-1 CrossRefGoogle Scholar
  43. 43.
    Sodhro AH, Pirbhulal S, Qaraqe M, Lohano S, Sodhro GH, Junejo NUR, Luo Z (2018) Power control algorithms for media transmission in remote healthcare systems. IEEE Access 6:42384–42393CrossRefGoogle Scholar
  44. 44.
    Sodhro AH, Shaikh FK, Pirbhulal S, Lodro MM, Shah MA (2017) Medical-QoS based telemedicine service selection using analytic hierarchy process. In: Khan S, Zomaya A, Abbas A (eds) Handbook of large-scale distributed computing in smart healthcare. Scalable computing and communications. Springer, Cham, pp 589–609Google Scholar
  45. 45.
    Magsi H, Sodhro AH, Chachar FA, Abro SAK, Sodhro GH, Pirbhulal S (2018) Evolution of 5G in Internet of medical things. In: 2018 international conference on computing, mathematics and engineering technologies (iCoMET). IEEE, pp 1–7Google Scholar
  46. 46.
    Sodhro AH, Pirbhulal S, Sodhro GH, Gurtov A, Muzammal M, Luo Z (2018) A joint transmission power control and duty-cycle approach for smart healthcare system. IEEE Sens J.  https://doi.org/10.1109/JSEN.2018.2881611 CrossRefGoogle Scholar
  47. 47.
    Sabatier JATMJ, Agrawal OP, Machado JT (2007) Advances in fractional calculus, vol 4. Springer, DordrechtCrossRefzbMATHGoogle Scholar
  48. 48.
    Machado JT, Kiryakova V, Mainardi F (2011) Recent history of fractional calculus. Commun Nonlinear Sci Numer Simul 16(3):1140–1153CrossRefMathSciNetzbMATHGoogle Scholar
  49. 49.
    Baleanu D (2012) Fractional calculus: models and numerical methods, vol 3. World Scientific, SingaporeCrossRefzbMATHGoogle Scholar
  50. 50.
    Baleanu D, Machado JAT, Luo AC (eds) (2011) Fractional dynamics and control. Springer, BerlinzbMATHGoogle Scholar
  51. 51.
    Atangana A, Baleanu D (2017) Caputo-Fabrizio derivative applied to groundwater flow within confined aquifer. J Eng Mech 143(5):D4016005CrossRefGoogle Scholar
  52. 52.
    Chaudhary NI, Zubair S, Raja MAZ, Dedovic N (2019) Normalized fractional adaptive methods for nonlinear control autoregressive systems. Appl Math Model 66:457–471CrossRefMathSciNetGoogle Scholar
  53. 53.
    Chaudhary NI, Raja MAZ, Aslam MS, Ahmed N (2018) Novel generalization of Volterra LMS algorithm to fractional order with application to system identification. Neural Comput Appl 29(6):41–58CrossRefGoogle Scholar
  54. 54.
    Chaudhary NI, Manzar MA, Raja MAZ (2018) Fractional Volterra LMS algorithm with application to Hammerstein control autoregressive model identification. Neural Comput Appl.  https://doi.org/10.1007/s00521-018-3362-z CrossRefGoogle Scholar
  55. 55.
    Couceiro MS, Rocha RP, Ferreira NF, Machado JT (2012) Introducing the fractional-order Darwinian PSO. SIViP 6(3):343–350CrossRefGoogle Scholar
  56. 56.
    Raja MAZ, Samar R, Manzar MA, Shah SM (2017) Design of unsupervised fractional neural network model optimized with interior point algorithm for solving Bagley–Torvik equation. Math Comput Simul 132:139–158CrossRefMathSciNetGoogle Scholar
  57. 57.
    Lodhi S, Manzar MA, Raja MAZ (2019) Fractional neural network models for nonlinear Riccati systems. Neural Comput Appl 31(1):359–378CrossRefGoogle Scholar
  58. 58.
    Couceiro MS, Machado JT, Rocha RP, Ferreira NM (2012) A fuzzified systematic adjustment of the robotic Darwinian PSO. Robot Autonom Syst 60(12):1625–1639CrossRefGoogle Scholar
  59. 59.
    Wang YY, Zhang H, Qiu CH, Xia SR (2018) A novel feature selection method based on extreme learning machine and fractional-order darwinian PSO. Comput Intell Neurosci, 2018Google Scholar
  60. 60.
    Akbar S, Zaman F, Asif M, Rehman AU, Raja MAZ (2018) Novel application of FO-DPSO for 2-D parameter estimation of electromagnetic plane waves. Neural Comput Appl.  https://doi.org/10.1007/s00521-017-3318-8 CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringCOMSATS University Islamabad, Attock CampusAttockPakistan
  2. 2.Department of Electrical EngineeringInternational Islamic UniversityIslamabadPakistan
  3. 3.School of Electrical and Electronic EngineeringUniversity of AdelaideAdelaideAustralia

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