Active Noise Cancellation System in Automobile Cabins Using an Optimized Adaptive Step-Size FxLMS Algorithm

Abstract

Active noise cancellation systems (ANCs) are employed to reduce or virtually eliminate the noise produced in the subject's vicinity. The Filtered-X Least Mean Square (FxLMS) is an adaptive algorithm that hands down the best and most convenient option for realizing this ambition since it is highly regarded for its reduced computational complexity and design robustness for use in a controller in an adaptive filter. An adaptive filter differs from a conventional filter given that it has a dynamic mode of operation, which involves the use of adaptive algorithms. These algorithms are used in applications ranging from system identification to the cancellation of unwanted noise. Here, we have performed a comparative study between our proposed design and the conventional design. In this paper, we endeavor to apply the FxLMS algorithm to tackle noise generated by vehicular traffic, including vehicle combustion engines, which usually lie in the narrowband frequency spectrum. The proposed system uses adaptive learning parameters such as adaptive step size to increment the rate of convergence and the speed of reduction of noise. We have made use of narrowband internal combustion engine white noise as our source noise signal for simplicity and convenience, which has been randomly generated during simulations.