A novel application of kernel adaptive filtering algorithms for attenuation of noise interferences
- 48 Downloads
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.
KeywordsAdaptive 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.
All authors declared that there is no material that required informed consent.
- 1.Harris CM (1991) Handbook of acoustical measurements and noise control. McGraw-Hill, New York, pp 30–45Google Scholar
- 3.Hänsler E, Schmidt G (2005) Acoustic echo and noise control: a practical approach, vol 40. Wiley, New YorkGoogle Scholar
- 4.Kuo SM, Morgan D (1995) Active noise control systems: algorithms and DSP implementations. Wiley, New YorkGoogle Scholar
- 29.Liu W, Principe JC, Haykin S (2011) Kernel adaptive filtering: a comprehensive introduction, vol 57. Wiley, New YorkGoogle 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 589–609Google 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 1–7Google 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, 2018Google Scholar