A Modified Gain Normalized Step Size Adaptive Algorithm for Improved Online Secondary Path Modelling in Active Noise Control
Asutosh Kar, Gagandeep Singh, Pradeep K. Shill, Somanath Pradhan, Vasundhara, Mads Græsbøll Christensen
Abstract
The noise cancellation performance of an active control system decreases when there are temporal variations in the primary and secondary paths. An active noise control (ANC) framework has been introduced in this work, which incorporates four adaptive filters and two decorrelation filters for online secondary path modelling. A novel adaptive algorithm for an active noise control filter has been developed with the combination of modified gain filtered-x recursive least square and normalised step size filtered-x least mean square. The aim is to improve the reduction of mean noise and decrease residual noise while maintaining consistent convergence rate. To update the decorrelation filters in the framework, an adaptive variable step size modified decorrelation normalised least mean square algorithm has been used. These filters are designed to maximize the efficiency of secondary path modelling. Compared to its counterparts, the simulation results illustrate the enhancements of the proposed framework without a substantial increase in overall computational complexity.
BibTeX
@inproceedings{icassp2025_amodifiedgainnor,
title = {A Modified Gain Normalized Step Size Adaptive Algorithm for Improved Online Secondary Path Modelling in Active Noise Control},
author = {Asutosh Kar and Gagandeep Singh and Pradeep K. Shill and Somanath Pradhan and Vasundhara and Mads Græsbøll Christensen},
booktitle = {ICASSP 2025},
year = {2025}
}