Study of non-linear filter-based algorithms for active noise control of machinary noises

Abstract

Sometimes, machinary noise comprises of non-linear deterministic noise. Therefore, it is expected that for such cases of noises, non-linear filter based active noise control algorithms can provide improved performance in terms of reduction of noise. Various non-linear filter based algorithms explored in this paper are: Volterra filtered-x least mean square algorithm, Hammerstein algorithm, Fourier algorithm utilising Fourier non-linear filter, even mirror Fourier non-linear filter and CN filter, bilinear algorithm, Legendre algorithm, filtered-s least mean square using functional link artifical neural network (FLANN) filter, generalised FLANN, exponential FLANN and recursive FLANN. Noise from various workshop machines like band saw, compressor and welding process were recorded using a microphone and utilised to study the algorithms. Two cases of primary paths, (a) linear primary path and (b) non-linear primary path with cubic non-linearity, are considered for simulation study. Performance of various algorithms is evaluated based on steady-state residual noise.