A cascaded IIR–FIR adaptive ANC system with output power constraints

Kozacky, Walter J.; Ogunfunmi, Tokunbo

doi:10.1016/j.sigpro.2013.06.036

Cited by 14 publications

(4 citation statements)

References 18 publications

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“…As the filter reaches the desired steady-state value, the algorithm must reduce the step-size μ to reduce the excess MSE at the error microphone. The practical challenge, on the other hand, is to develop a set of criteria for altering the step-size μ so that the adaptive filter creates a tiny surplus MSE while maintaining its ability to respond quickly to changes in signal characteristics (Kozacky and Ogunfunmi, 2014).…”

Section: Proposed Adaptive Step-size Fxlms Algorithm (Asfxlms)mentioning

confidence: 99%

Experimental Evaluation of Adaptive Step-Size Based FxLMS Approach for Active Noise Control in Magnetic Resonance Imaging Systems

Juvanna¹,

Ramachandraiah²,

Muthukumaran³

et al. 2022

Journal of Computer Science

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Magnetic Resonance Imaging (MRI) scanners emit up to 135 decibels of acoustic noise, which is a major source of discomfort for patients and personnel evaluating them during routine medical scans, necessitating the development of a method to reduce the acoustic noise generated during MRI testing. The goal of this study is to propose a frequency-domain Active Noise Control (ANC) method for acoustic noise reduction in MRI and to demonstrate its ANC effectiveness on an experimental MRI scanner model specifically built for this purpose. In comparison to the standard Least Mean Square (LMS) algorithm, we used the Filtered-x Least Mean Square (FxLMS) approach with an adaptive variable step-size approach to adjust the filter coefficients dynamically, which considerably enhances the ANC system's convergence and reduces acoustic noise. The simulation results obtained from the MATLAB Simulink model on a pre-recorded 30-sec MRI noise signal represented by the step-size variation over time, error and noise convergence plots reveal that the adaptive step-size FxLMS (ASFxLMS) technique increases noise and error convergence rate significantly more than existing ANC algorithms to facilitate its use during MRI scans. Experimental results with our functional MRI (fMRI) testbed show approximately 25-dB overall noise reduction relative to the noise levels without ANC.

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Section: Proposed Adaptive Step-size Fxlms Algorithm (Asfxlms)mentioning

confidence: 99%

Experimental Evaluation of Adaptive Step-Size Based FxLMS Approach for Active Noise Control in Magnetic Resonance Imaging Systems

Juvanna¹,

Ramachandraiah²,

Muthukumaran³

et al. 2022

Journal of Computer Science

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“…The FxLMS-based algorithm can effectively tackle the noise from NANC systems. So far, a large number of FxLMS improvements have been proposed and analyzed for NANC, see [157,158,25,159,160,161,162,163,164,165]. In Table 3, we summarize the development of FxLMS analysis in the context of NANC systems before the past decade.…”

Section: ) Fxlms-based Algorithms For Narrowband Noisementioning

confidence: 99%

A survey on active noise control in the past decade—Part I: Linear systems

et al. 2021

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Active noise control (ANC) is an effective way for reducing the noise level in electroacoustic or electromechanical systems. Since its first introduction in 1936, this approach has been greatly developed. This paper focuses on discussing the development of ANC techniques over the past decade. Linear ANC algorithms, including the celebrated filtered-x least-mean-square (FxLMS)based algorithms and distributed ANC algorithms, are investigated and evaluated. Nonlinear ANC (NLANC) techniques, such as functional link artificial neural network (FLANN)-based algorithms, are pursued in Part II. Furthermore, some novel methods and applications of ANC emerging in the past decade are summarized. Finally, future research challenges regarding the ANC technique are discussed.

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“…Therefore, the leakage factor potentially causes instability to the ANC system and requires unnecessary complexity for practical implementation. Other algorithms based on penalty method even incurs higher computational complexity [23,25,27,28,40,41].…”

Section: Cost Function Modificationmentioning

confidence: 99%