Multiple Iteration of Weight Updates for Least Mean Square Adaptive Filter in Active Noise Control Application

Rahimie Mustafa,Shahrizal Jelani,Ahmad Nur Alifa Abdul Razap,Anuar Mikdad Muad,H.L Yuan,P Tandon,R.K Agarwal,E.X Wang

doi:10.1051/matecconf/20179514006

Rahimie Mustafa, Shahrizal Jelani + Show 6 more

Open Access

https://doi.org/10.1051/matecconf/20179514006

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Abstract

The method of least mean square (LMS) is the commonly used algorithm in Adaptive filter due to its simplicity and robustness in implementation. In Active Noise Control application, a filtered reference signal is used prior to LMS algorithm to overcome the constraint on stability and convergence performance of the system due to the existence of the auxiliary path. This is known as Filtered-X LMS algorithm. In conventional Filtered-X LMS algorithm, each filter weight is updated once on every audio sample. This paper proposes the improved version of Filtered-X LMS algorithm with the use of multiple iteration of filter weight on every sample of audio signal. The proposed work uses field programmable gate arrays to realize real-time simulation on hardware for the noise signal of 500 Hz. Results from the real-time hardware simulations have shown much faster error convergence and better adaptation performance for different selections of learning constant μ, as compared with the conventional method.

Highlights

The least mean square (LMS) can be considered as the most applied algorithm for linear adaptive filter in active noise control (ANC) application that required real-time processing for a successful and efficient hardware implementation [1]-[3]
The use of specialized digital signal processor (DSP) chip and with the capability of handling numerous floating point operations manage to address the real–time processing issue in narrowband attenuation of ANC headset [4]-[5], and in broadband attenuation for duct application [6] based on least mean square (LMS) algorithm
The performance of signal convergence in terms of the speed of filter adaptation and the excess of mean square error (MSE) is usually overlooked, which can be critical in a time time-varying environment [2], [3]

Summary

Introduction

The least mean square (LMS) can be considered as the most applied algorithm for linear adaptive filter in active noise control (ANC) application that required real-time processing for a successful and efficient hardware implementation [1]-[3]. LMS algorithm is an approximation method of steepest gradient descent that relies on the value of the instantaneous squared error signal [18], [19] Due to this approximation, the calculation of adaptive filter weight has resulted in the simplification that is expressed as: w(n + 1) = w(n) + P x(n)e(n). Numerical calculation for updating the filter weights utilizing conventional DSP chip is performed one time in every sample of audio signal that is expressed as [1]: wk (n 1) wk (n) Px(n k)e(n).

Learning constant

Results

Conclusion