Online acoustic feedback mitigation with improved noise‐reduction performance in active noise control systems

Ahmed, Shakeel; Akhtar, Muhammad Tahir; Zhang, Xi

doi:10.1049/iet-spr.2012.0204

Cited by 21 publications

(8 citation statements)

References 20 publications

(57 reference statements)

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“…In order to verify the effectiveness of the proposed algorithm, simulation results are presented to compare the performance of the proposed method with the existing method [9, 12]. In the simulation, we used the primary path and secondary path of the experimental data provided in [6]. The sampling frequency in this simulation was 2 kHz.…”

Section: Simulation Results and Comparative Analysismentioning

confidence: 99%

“…Therefore, in order to ensure the control accuracy and stability of the control system, it is of great practical significance to use the online identification of the secondary path. There are following two kinds of on‐line secondary channel identification: one is the simultaneous equation of the control signal itself and the other is the identification of the superposition noise at the output end [3–9]. The method of simultaneous equations can influence the control effect and convergence of the control system, which is currently in the theoretical simulation stage and has great limitations for practical application.…”

Section: Introductionmentioning

confidence: 99%

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Variable step strategy for online secondary path modelling in active vibration control systems

Yang

2018

J. eng.

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Section: Simulation Results and Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Variable step strategy for online secondary path modelling in active vibration control systems

Yang

2018

J. eng.

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“…Using (4) with z = 1, the final correction term at a given iteration can be written as

normalΔ {bold-italicw}_{bold-italicf} false(n false) = μ_{f} {bold-italicu}^{normalT} false(n false) f (][, d false(n false) - u false(n false) w false(n - 1 false)) ⊙ \frac{{bold-italicw}_{bold-italicl}^{1 - v} false(n false)}{normalΓ false(2 - v false)}

where ʘ denotes element‐wise multiplication. For traditional LMS, a time‐varying optimised step size is chosen for stability and performance and is given as [2, 3]

μ_{l} false(n false) = \frac{μ_{l}}{{∥bold-italicu (n)∥}^{2}}

Similarly, the step size can be adapted using the fractional derivative to minimise the objective function ‖ w ( n + 1) − w ( n ) 2 ‖ subject to the constraint: d ( n ) = w T ( n + 1) u ( n ). The step size parameter can be adjusted according to the fractional order as follows:

μ_{normalf} false(n false) = \frac{normalΓ false(3 - v false)}{normalΓ false(2 - v false) normalΓ false(3 false)} \frac{μ_{f}}{{(∥∥, u false(n false))}^{2}}

Using (2) and (6)–(8), the sequence of execution of weight update for the FN‐FeLMS algorithm is summarised as in Table 1.…”

Section: System Modelmentioning

confidence: 99%

“…where ʘ denotes element-wise multiplication. For traditional LMS, a time-varying optimised step size is chosen for stability and performance and is given as [2,3] m…”

mentioning

confidence: 99%

Fractional normalised filtered‐error least mean squares algorithm for application in active noise control systems

et al. 2014

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A novel fractional normalised filtered-error least mean squares (FN-FeLMS) algorithm is designed for secondary path modelling in active noise control systems. The update is formed as a combination of the conventional LMS and a fractional update derived from the Riemann-Liouville differintegral operator. The algorithm is considered for (machine) noise reduction for a primary path with zeromean binary or Gaussian sources as inputs. An anti-noise signal is generated to alleviate the effect of noise and to minimise the filtered error by improved secondary path modelling. The proposed arrangement is evaluated for a number of different scenarios by varying the step size and fractional orders. Simulation results show that the proposed technique is more robust to step size variation; it outperforms the traditional FeLMS approach in terms of convergence, model accuracy and steady-state performance for a given signal-to-noise ratio.Introduction: Active noise control (ANC) systems operate by generating a (destructive) interference signal through secondary path models to cancel the effect of noise. ANC can be employed in numerous applications, including personal hearing devices, duct and room acoustics enhancement, engine exhaust noise suppression and improving acoustics in vehicle enclosures, aircraft cabins and vibrating machines [1]. Usually, the filtered-x least mean squares algorithm and its variants are used for ANC problems [2], with computational complexity comparable with the LMS algorithm. An improved version of the algorithm, i.e. filtered-error LMS (FeLMS) is also widely used, for which filtering of the input signal is not required [3] and an adaptive step size strategy is devised to improve initial convergence, whereas noise is reduced in the steady state by reducing the step size [4]. There have been a number of applications of fractional calculus in the recent past in adaptive control and signal processing [5,6]. The main contribution of this Letter is to investigate the use of fractional calculus concepts in ANC systems; both positive and negative fractional orders are employed based on the differintegral operator [6]. A new fractional normalised FeLMS (FN-FeLMS) algorithm, which exploits fractional derivatives, is derived for minimising the mean squared error (MSE).

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“…A power scheduling strategy was imposed for the auxiliary noise used for modelling the feedback path to meet the conflicting requirement of faster convergence of feedback path modelling and lower steady-state residual error [13,14]. A robust variable step size method for feedback path modelling and neutralization in a single channel narrowband system is proposed to achieve faster convergence [15].…”

Section: Introductionmentioning

confidence: 99%

A Four-Stage Method for Active Control with Online Feedback Path Modelling Using Control Signal

Pradhan

Qiu

2019

Applied Sciences

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The presence of control signal feedback to the reference microphone in feedforward active control systems deteriorates the control performance. A four-stage method is proposed in this paper to carry out online feedback path modelling with the control signal. It consists of controller initialization, feedback path modelling using decorrelation filters, active control operation, and feedback path change detection for maintaining the control operation. In contrast to the existing auxiliary noise injection method, the proposed method uses five switches and three thresholds to control and maintain the system stability by avoiding the interference between control operation and feedback path modelling, and adaptive decorrelation filters are used to increase the feedback path modelling performance. Simulation results reveal that the proposed method is capable of tracking feedback path changes without injecting any auxiliary noise and maintaining the noise reduction performance and stability of the system.

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Online acoustic feedback mitigation with improved noise‐reduction performance in active noise control systems

Cited by 21 publications

References 20 publications

Variable step strategy for online secondary path modelling in active vibration control systems

Variable step strategy for online secondary path modelling in active vibration control systems

Fractional normalised filtered‐error least mean squares algorithm for application in active noise control systems

A Four-Stage Method for Active Control with Online Feedback Path Modelling Using Control Signal

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