Skip to main content
SpringerLink
Log in

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

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

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.

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
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Harris CM (1991) Handbook of acoustical measurements and noise control. McGraw-Hill, New York, pp 30–45

    Google Scholar 

  2. Boll S (1979) Suppression of acoustic noise in speech using spectral subtraction. IEEE Trans Acoust Speech Signal Process 27(2):113–120

    Article  Google Scholar 

  3. Hänsler E, Schmidt G (2005) Acoustic echo and noise control: a practical approach, vol 40. Wiley, New York

    Google Scholar 

  4. Kuo SM, Morgan D (1995) Active noise control systems: algorithms and DSP implementations. Wiley, New York

    Google Scholar 

  5. Elliott SJ, Nelson PA (1993) Active noise control. IEEE Signal Process Mag 10(4):12–35

    Article  Google Scholar 

  6. Kuo SM, Morgan DR (1999) Active noise control: a tutorial review. Proc IEEE 87(6):943–973

    Article  Google Scholar 

  7. George NV, Panda G (2013) Advances in active noise control: a survey, with emphasis on recent nonlinear techniques. Sig Process 93(2):363–377

    Article  Google Scholar 

  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):454–465

    Article  Google Scholar 

  9. Bjarnason E (1995) Analysis of the filtered-X LMS algorithm. IEEE Trans Speech Audio Process 3(6):504–514

    Article  Google Scholar 

  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):720–726

    Article  Google Scholar 

  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 107:433–443

    Article  Google Scholar 

  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):973–975

    Article  Google Scholar 

  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):1363–1376

    Article  MathSciNet  Google Scholar 

  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 120:150–165

    Article  Google Scholar 

  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):267–281

    Article  Google Scholar 

  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):2315–2327

    Article  Google Scholar 

  17. Rout NK, Das DP, Panda G (2016) Particle swarm optimization based nonlinear active noise control under saturation nonlinearity. Appl Soft Comput 41:275–289

    Article  Google Scholar 

  18. George NV, Panda G (2012) A particle-swarm-optimization-based decentralized nonlinear active noise control system. IEEE Trans Instrum Meas 61(12):3378–3386

    Article  Google Scholar 

  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 73:666–683

    Article  Google Scholar 

  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):246–259

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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):89–99

    MathSciNet  Google Scholar 

  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):1465–1479

    Article  Google Scholar 

  24. Arqub OA (2017) Adaptation of reproducing kernel algorithm for solving fuzzy Fredholm–Volterra integrodifferential equations. Neural Comput Appl 28(7):1591–1610

    Article  Google Scholar 

  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):2595–2606

    Article  Google Scholar 

  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

    Article  Google Scholar 

  27. Liu W, Pokharel PP, Principe JC (2008) The kernel least-mean-square algorithm. IEEE Trans Signal Process 56(2):543–554

    Article  MathSciNet  MATH  Google Scholar 

  28. Chen B, Zhao S, Zhu P, Príncipe JC (2012) Mean square convergence analysis for kernel least mean square algorithm. Signal Process 92(11):2624–2632

    Article  Google Scholar 

  29. Liu W, Principe JC, Haykin S (2011) Kernel adaptive filtering: a comprehensive introduction, vol 57. Wiley, New York

    Google Scholar 

  30. Tobar FA, Kung SY, Mandic DP (2014) Multikernel least mean square algorithm. IEEE Trans Neural Netw Learn Syst 25(2):265–277

    Article  Google Scholar 

  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):1867–1878

    Article  Google Scholar 

  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):1–13

    Article  Google Scholar 

  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):999–1013

    Article  MathSciNet  MATH  Google Scholar 

  34. Yazdi HS, Pakdaman M, Modaghegh H (2011) Unsupervised kernel least mean square algorithm for solving ordinary differential equations. Neurocomputing 74(12–13):2062–2071

    Article  Google Scholar 

  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

    Article  Google Scholar 

  36. Chaudhary NI, Raja MAZ (2015) Identification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithms. Nonlinear Dyn 79(2):1385–1397

    Article  MathSciNet  MATH  Google Scholar 

  37. Chaudhary NI, Raja MAZ (2015) Design of fractional adaptive strategy for input nonlinear Box–Jenkins systems. Signal Process 116:141–151

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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):2355–2369

    Article  Google Scholar 

  41. Xie X, Li B, Chai X (2017) A manifold framework of multiple-kernel learning for hyperspectral image classification. Neural Comput Appl 28(11):3429–3439

    Article  Google Scholar 

  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

    Article  Google Scholar 

  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 6:42384–42393

    Article  Google Scholar 

  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 589–609

    Google Scholar 

  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 1–7

  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

    Article  Google Scholar 

  47. Sabatier JATMJ, Agrawal OP, Machado JT (2007) Advances in fractional calculus, vol 4. Springer, Dordrecht

    Book  MATH  Google Scholar 

  48. Machado JT, Kiryakova V, Mainardi F (2011) Recent history of fractional calculus. Commun Nonlinear Sci Numer Simul 16(3):1140–1153

    Article  MathSciNet  MATH  Google Scholar 

  49. Baleanu D (2012) Fractional calculus: models and numerical methods, vol 3. World Scientific, Singapore

    Book  MATH  Google Scholar 

  50. Baleanu D, Machado JAT, Luo AC (eds) (2011) Fractional dynamics and control. Springer, Berlin

    MATH  Google Scholar 

  51. Atangana A, Baleanu D (2017) Caputo-Fabrizio derivative applied to groundwater flow within confined aquifer. J Eng Mech 143(5):D4016005

    Article  Google Scholar 

  52. Chaudhary NI, Zubair S, Raja MAZ, Dedovic N (2019) Normalized fractional adaptive methods for nonlinear control autoregressive systems. Appl Math Model 66:457–471

    Article  MathSciNet  Google Scholar 

  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):41–58

    Article  Google Scholar 

  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

    Article  Google Scholar 

  55. Couceiro MS, Rocha RP, Ferreira NF, Machado JT (2012) Introducing the fractional-order Darwinian PSO. SIViP 6(3):343–350

    Article  Google Scholar 

  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 132:139–158

    Article  MathSciNet  Google Scholar 

  57. Lodhi S, Manzar MA, Raja MAZ (2019) Fractional neural network models for nonlinear Riccati systems. Neural Comput Appl 31(1):359–378

    Article  Google Scholar 

  58. Couceiro MS, Machado JT, Rocha RP, Ferreira NM (2012) A fuzzified systematic adjustment of the robotic Darwinian PSO. Robot Autonom Syst 60(12):1625–1639

    Article  Google Scholar 

  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, 2018

  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

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, COMSATS University Islamabad, Attock Campus, Attock, Pakistan

    Muhammad Asif Zahoor Raja, Zaheer Ahmed & Ata Ur Rehman

  2. Department of Electrical Engineering, International Islamic University, Islamabad, Pakistan

    Naveed Ishtiaq Chaudhary

  3. School of Electrical and Electronic Engineering, University of Adelaide, Adelaide, Australia

    Muhammad Saeed Aslam

Authors
  1. Muhammad Asif Zahoor Raja
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Naveed Ishtiaq Chaudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zaheer Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ata Ur Rehman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Muhammad Saeed Aslam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Saeed Aslam.

Ethics declarations

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.

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

Raja, M.A.Z., Chaudhary, N.I., Ahmed, Z. et al. A novel application of kernel adaptive filtering algorithms for attenuation of noise interferences. Neural Comput & Applic 31, 9221–9240 (2019). https://doi.org/10.1007/s00521-019-04390-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04390-8

Keywords

Search

Navigation