Recent advances on active noise control: open issues and innovative applications

Kajikawa, Yoshinobu; Gan, Woon‐Seng; Kuo, Sen M.

doi:10.1017/atsip.2012.4

Cited by 304 publications

(136 citation statements)

References 240 publications

(310 reference statements)

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“…Filtered-x Least Mean Square (FxLMS) [1,2] is widely used in various adaptive signal processing applications, such as Active Noise Control (ANC) [3,4]. In these applications, there is a primary signal that propagates away from an unknown source to an error point through an unknown primary path.…”

Section: Introductionmentioning

confidence: 99%

Efficient FxLMS algorithm with simplified secondary path models

Ardekani

Sharifzadeh

Rehman

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper extends the existing work on the root locus analysis of FxLMS algorithm by considering secondary path modeling errors. Rules for sketching FxLMS root locus are set out. An analytic convergence condition is then derived from the root locus plot. A deliberately-misaligned secondary path model is proposed to be used as the data preparation filter in the FxLMS algorithm. The proposed filter increases the computational efficiency of the algorithm, without changing its convergence behavior. The theoretical results are verified in practice by busing an experimental system.

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Section: Introductionmentioning

confidence: 99%

Efficient FxLMS algorithm with simplified secondary path models

Ardekani

Sharifzadeh

Rehman

et al. 2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Active noise control (ANC) [1,2] is a field that still presents challenges and open problems for researchers [3,4], especially with regard to the limitations of the hardware used in its implementation. ANC systems are commonly developed using FIR adaptive filters; therefore, for real-time applications, it is very important to choose a suitable adaptive algorithm.…”

Section: Introductionmentioning

confidence: 99%

Multichannel Filtered-X Error Coded Affine Projection-Like Algorithm with Evolving Order

Avalos

Aguilar

Martínez

et al. 2017

Shock and Vibration

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Affine projection (AP) algorithms are commonly used to implement active noise control (ANC) systems because they provide fast convergence. However, their high computational complexity can restrict their use in certain practical applications. The Error Coded Affine Projection-Like (ECAP-L) algorithm has been proposed to reduce the computational burden while maintaining the speed of AP, but no version of this algorithm has been derived for active noise control, for which the adaptive structures are very different from those of other configurations. In this paper, we introduce a version of the ECAP-L for single-channel and multichannel ANC systems. The proposed algorithm is implemented using the conventional filtered-x scheme, which incurs a lower computational cost than the modified filtered-x structure, especially for multichannel systems. Furthermore, we present an evolutionary method that dynamically decreases the projection order in order to reduce the dimensions of the matrix used in the algorithm's computations. Experimental results demonstrate that the proposed algorithm yields a convergence speed and a final residual error similar to those of AP algorithms. Moreover, it achieves meaningful computational savings, leading to simpler hardware implementation of real-time ANC applications.

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“…For example, it may be sensible to modify the spectral shape of the disturbance noise in automobiles [8], [9], aircraft [10], [11], air-conditioning systems [12], [13], or incubators [14] in order to control subjective metrics such as loudness, sharpness, or roughness [15] researchers have modified the active control strategy so that the central focus is not only on cancellation, but could also be on either the attenuation or enhancement of a primary disturbance signal. This process is called active sound profiling [16] or active noise equalization [17].…”

Section: Introductionmentioning

confidence: 99%

Modified Phase-Scheduled-Command FxLMS Algorithm for Active Sound Profiling

Patel

Cheer

George

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Active sound profiling, or active noise equalization strategies have been proposed to achieve spectral shaping of a primary disturbance signal. The control algorithms proposed to achieve such spectral shaping have either suffered from poor robustness to plant modelling uncertainties or required high levels of control effort. To improve the robustness of active sound profiling to uncertainties in the plant model, whilst avoiding increased control effort, a modified phase-scheduledcommand filtered-x least-mean-square (FxLMS) algorithm is proposed in this paper. The new algorithm provides improved stability, whilst requiring the minimum control effort. This improvement is achieved by replacing the plant model with an intelligent adaptive-hysteresis switching mechanism to allow the necessary estimation of the disturbance signal phase. The improved performance and robustness of the proposed algorithm is demonstrated through a series of simulations using measured acoustic responses.

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Recent advances on active noise control: open issues and innovative applications

Cited by 304 publications

References 240 publications

Efficient FxLMS algorithm with simplified secondary path models

Efficient FxLMS algorithm with simplified secondary path models

Multichannel Filtered-X Error Coded Affine Projection-Like Algorithm with Evolving Order

Modified Phase-Scheduled-Command FxLMS Algorithm for Active Sound Profiling

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