Method to update the coefficients of the secondary path filter under active noise control

Fujii, Kensaku; Ohga, Juro

doi:10.1016/s0165-1684(00)00214-0

Cited by 27 publications

(12 citation statements)

References 7 publications

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“…Several algorithms using additional adaptive filters were developed to solve such problems with increased computational complexity. They include the overall online modeling algorithm [40][41][42] and simultaneous equations methods [43][44][45], which do not use an additive random noise. The overall online secondary-path modeling algorithm introduces an additional adaptive filter for estimating the primary path.…”

Section: B) Methods For Improvementmentioning

confidence: 99%

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

Kajikawa

Gan

Kuo

2012

SIP

304

113

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The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

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Section: B) Methods For Improvementmentioning

confidence: 99%

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

Kajikawa

Gan

Kuo

2012

SIP

304

113

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“…Auxiliary noise power scheduling was first proposed in the work of Zhang et al 10 In the work of Akhtar et al, 11 a variable step size is introduced that increases as ANC converges. 15 Chang et al 16,17 propose to decouple estimation of the SP at different frequencies for narrowband ANC. Other related strategies are proposed in the work of Ahmed et al 14 The simultaneous equation method is proposed in the work of Fujii and Ohga.…”

Section: State-of-the-artmentioning

confidence: 99%

“…(11) p 0 = from the periodogram (12) p(0) = w(0) =p 0 (13) q j (0) = 1 (14) = small number (15) If the controller of the MFxLMS (w 1,i ) has a significantly larger magnitude than the controller of MMFxLMS (w 2,i ), then this probably means that an incorrectly modeled SP is leading the MFxLMS to divergence.…”

Section: Fxlms Algorithmmentioning

confidence: 99%

“…Other related strategies are proposed in the work of Ahmed et al 14 The simultaneous equation method is proposed in the work of Fujii and Ohga. 15 Chang et al 16,17 propose to decouple estimation of the SP at different frequencies for narrowband ANC. Finally, the work of Padhi et al 18 compare the performance of several hybrid ANC systems with online SP modeling.…”

Section: State-of-the-artmentioning

confidence: 99%

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A narrowband active noise control system with reference synthesis

Lopes

Gerald

2019

Adaptive Control & Signal

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Most active noise control (ANC) algorithms require a model of the secondary path (SP). If there are large changes in the SP, online SP modeling algorithms may not be able to follow the changes quickly enough to prevent divergence. In this work, the mirror-MFxLMS (MMFxLMS) is adapted to narrowband ANC, and the resulting algorithm is analyzed. The MMFxLMS is an algorithm similar to the MFxLMS and FxLMS but has online SP modeling; it is stable for any SP estimate and does not require auxiliary noise. In addition, a technique to alternate between MFxLMS and MMFxLMS is proposed, resulting in the M 3 FxLMS algorithm, which does not sacrifice performance and assures stability. However, the M 3 FxLMS requires a reference signal that is not available in feedback ANC.Feedback ANC is important in many practical cases, for instance, in ANC headphones. To solve this problem, a technique is proposed to synthesize a reference formed by a sum of sinusoids using measurements from the error sensor signal.This technique uses the fast Fourier transform to obtain estimates of the harmonic frequencies of the periodic noise components, and then refines and tracks these using a phase-locked loop (PLL). The resulting algorithm enables a stable ANC system, even under large and sudden changes in the SP. The proposed PLL is also new and has a lock-in range equal to the full band of the system. KEYWORDS active noise control (ANC), feedback, FxLMS, narrowband, PLL, stability INTRODUCTIONActive noise control (ANC) is a technique where waves (antinoise) with opposite phase to a noise signal are used to reduce the noise signal level. 1 In feedforward ANC, a signal called the reference signal is used to generate the antinoise signal, using additional information from a set of error sensors that indicates the residual noise level. In feedback ANC, the signal from a set of error sensors is used to generate the antinoise signal. In narrowband ANC, the noise to be canceled is assumed to be periodic or a sum of periodic signals. This paper addresses single-channel (one error sensor) narrowband feedback ANC, with applications, for instance, in ANC headphones.Most ANC algorithms require a secondary path (SP) model and can become unstable if that model is not sufficiently accurate. The SP model can be obtained before the start of noise canceling (offline) or during operation (online). If the SP changes significantly during noise cancelation, online SP modeling is required. However, even with online SP modeling, if there is a large and sudden change, the model may not be updated quickly enough and the algorithm may become unstable. To solve this problem, the authors have previously proposed the mirror-MFxLMS (MMFxLMS) algorithm. 2 1118

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“…If the secondary path and its model are linear, a typical online model identification strategy is to add a small white noise training signal into the input of secondary path [8], [11], [12], but this additional training signal will result in an extra noise in residual error and thus degrade the noise cancellation effect. In recent years, some online model adjustment strategies without additional training signal have been proposed for linear secondary path model [13], [14]. However, all of these online adjustment strategies with or without training signal are not suitable for nonlinear model.…”

Section: Introductionmentioning

confidence: 99%

A robust adaptive cancellation scheme for nonlinear uncertain system based on extended plant model

Zeng

Lü

2009

2009 4th IEEE Conference on Industrial Electronics and Applications

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An extended control plant is introduced to substitute the plant used in ordinal adaptive cancellation system. All of the inputs and outputs of such an extended plant are online available and hence its model can be identified online conveniently. Based on the static nonlinear feedforward neural network model of this extended plant, a new adaptive cancellation system structure and algorithm are proposed. In this system, a dynamic nonlinear feedforward neural network is used for controller. The control error is back propagated through the extended plant model to update the connection weights of controller. It is shown by simulation results that the proposed adaptive cancellation scheme is effective for both linear and nonlinear system. Moreover, since both the plant model and controller are online adjusted simultaneously, this control scheme has good robustness for the variation of system parameters and features.Index Terms-adaptive cancellation, active noise control, nonlinear uncertain system, BP network, online system identification I.

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Method to update the coefficients of the secondary path filter under active noise control

Cited by 27 publications

References 7 publications

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

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

A narrowband active noise control system with reference synthesis

A robust adaptive cancellation scheme for nonlinear uncertain system based on extended plant model

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