Efficient Narrowband Noise Cancellation System Using Adaptive Line Enhancer

Ho, Chung-Ying; Shyu, Kuo-Kai; Chang, Cheng-Yuan; Kuo, Sen M.

doi:10.1109/taslp.2020.2982578

Cited by 27 publications

(8 citation statements)

References 24 publications

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“…e narrowband ANC (NANC) system includes two methods: feedforward and feedback ways [7,8]. e feedback ANC method applies error signal and then synthesizes the reference signal, which reduces estimated noise [9,10]. On the other hand, a feedforward system measures the noise using a reference sensor and thus is capable of cancelling broadband noise [11].…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

Huynh

Chang

2021

Mathematical Problems in Engineering

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Noise in a dynamic system is practically unavoidable. Today, such noise is commonly reduced using an active noise control (ANC) system with the filtered-x least mean square (FXLMS) algorithm. However, the performance of the ANC system with FXLMS algorithm is significantly impaired in nonlinear systems. Therefore, this paper develops an efficient nonlinear adaptive feedback neural controller (NAFNC) to eliminate narrowband noise for both linear and nonlinear ANC systems. The proposed controller is implemented to update its coefficients without prior offline training by neural network. Hence, the proposed method has rapid convergence rate as confirmed by simulation results. The proposed work also analyzes the stability and convergence of the proposed algorithm. Simulation results verify the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

Huynh

Chang

2021

Mathematical Problems in Engineering

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“…Many efforts have been made to improve these techniques: analyzing their convergence [21], adding mechanisms for online detection of noise frequencies [22], [23], getting better frequency resolution when the frequency separation between two adjacent tones is small [24], correcting the mismatch at those frequencies [25]- [27], or considering non-linearities M. Ferrer, M. De Diego, G. Piñero, A. Gonzalez are with the Institute of Telecommunications and Multimedia Applications (iTEAM), Universitat Politècnica de València (UPV), Spain, e-mail: mferrer, mdediego, gpinyero, agonzal@dcom.upv.es.…”

Section: Introduction and Problem Statementmentioning

confidence: 99%

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

Ferrer¹,

Diego²,

Hassani

et al. 2022

IEEE/ACM Trans. Audio Speech Lang. Process.

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In this work we propose a multi-channel active noise equalizer (ANE) that can deal with multi-frequency noise signals and assigns simultaneously different equalization gains to each frequency component at each monitoring sensor. For this purpose, we state a pseudo-error noise signal for each sensor of the ANE, which has to be cancelled out in order to get the desired equalization profiles. Firstly the optimal analytic solution for the ANE filters in the case of single-frequency noise is provided, and an adaptive algorithm based on the Least Mean Squared (LMS) is proposed for the same case. We also show that this adaptive strategy reaches the theoretical solution in steady state. Secondly, we state an equivalent approach for the case of multi-frequency noise based on two alternatives: a common pseudo-error signal at each sensor for all frequencies, and a different pseudo-error signal at each sensor for each frequency. The analytic and adaptive solutions for the ANE control filters have been developed for both pseudo-error alternatives. Finally, the ability of the proposed ANE to achieve simultaneously different user-selected noise profiles in different locations has been validated by their transfer functions and simulations.

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“…Other applications of ALE include speed sensors for wheels [18], detection of defect rails [19], suppression of beat noise in FM radio of motor vehicle [20], detection of bearing fault [21], and estimation of respiratory rate for post-intensive care patient monitoring using accelerometry [22]. Recently, ALE has been implemented for the development of an efficient narrow band noise cancellation system [23], using the IIR bandpass filter proposed by Kumar & Pal [14] in 1990. Bershad, et al [24] and Eweda, et al [25] considered the stochastic analysis of ALE with a cyclo-stationary input.…”

Section: Introductionmentioning

confidence: 99%

On Minimal Second-order IIR Bandpass Filters with Constrained Poles and Zeros

Joelianto

2021

J. Eng. Technol. Sci.

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In this paper, several forms of infinite impulse response (IIR) bandpass filters with constrained poles and zeros are presented and compared. The comparison includes the filter structure, the frequency ranges and a number of controlled parameters that affect computational efforts. Using the relationship between bandpass and notch filters, the two presented filters were originally developed for notch filters. This paper also proposes a second-order IIR bandpass filter structure that constrains poles and zeros and can be used as a minimal parameter adaptive digital second-order filter. The proposed filter has a wider frequency range and more flexibility in the range values of the adaptation parameters.

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Efficient Narrowband Noise Cancellation System Using Adaptive Line Enhancer

Cited by 27 publications

References 24 publications

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

Nonlinear Neural System for Active Noise Controller to Reduce Narrowband Noise

Multi-Tone Active Noise Equalizer With Spatially Distributed User-Selected Profiles

On Minimal Second-order IIR Bandpass Filters with Constrained Poles and Zeros

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