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


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.


Adaptive algorithms Active noise control Kernal LMS Activation functions 


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.


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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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