Auxiliary Noise Power Scheduling Algorithm for Active Noise Control with On-line Secondary Path Modelling and Sudden Changes

Lopes, Paulo A. C.; Gerald, José A. B.

doi:10.1109/lsp.2015.2415875

Cited by 21 publications

(22 citation statements)

References 18 publications

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“…Eriksson's method based on auxiliary noise is proposed. Selection of the auxiliary noise signal level by using the SP estimate and making it R times lower than the residual noise signal is proposed in the work of Carini and Malatini 12 and in the work of Lopes and Gerald, 13 the value of R is adapted as required. 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.…”

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%

“…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. Selection of the auxiliary noise signal level by using the SP estimate and making it R times lower than the residual noise signal is proposed in the work of Carini and Malatini 12 and in the work of Lopes and Gerald, 13 the value of R is adapted as required. 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%

“…In the case of p(n), this is not very important since x H (n)x(n) = N, and q(n) is set to a small constant value only to prevent division by zero when the outputs of the oscillators are still off. In the case of s(n), q(n) was selected to be sp q j (n)∕2 similarly to other works, 13,14,25 and as suggested by eq. 38 in the work of Lopes and Gerald.…”

Section: Fxlms Algorithmmentioning

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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Section: State-of-the-artmentioning

confidence: 99%

Section: Fxlms Algorithmmentioning

confidence: 99%

Section: State-of-the-artmentioning

confidence: 99%

Section: Fxlms Algorithmmentioning

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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“…They also included the secondary path estimation in their model. Recently, Lopes proposed a random walk Kalman filter [9]. It relies on white Gaussian noise signals to model state changes.…”

Section: Introductionmentioning

confidence: 99%

Time-domain Kalman filter for active noise cancellation headphones

Liebich

Fabry

Jax

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Noise pollution has a large negative influence on the health of humans, especially in case of long-term exposure. Various passive hearing protection approaches are available. However, they often lack good protection against low frequency noise. For these applications, the principle of Active Noise Cancellation (ANC) offers a promising supplement. It relies on anti-phase compensation of the noise signal. Within the area of ANC, only few publications deal with the Kalman filter approach. The state-of-the-art in literature is briefly reviewed. The algorithm presented in this contribution is inspired by the time-domain Kalman filter. The Kalman filter has the favorable property of fast convergence as well as good tracking properties. Especially the tracking of time-varying noise conditions is often a drawback of least-mean-square (LMS) and recursive-least-square (RLS) approaches. The proposed algorithm uses the Kalman equations which are extended by online model parameter estimation based on observable signals. This results in faster convergence and higher robustness against dynamically changing noise conditions. The performance of the algorithm is evaluated by means of convergence, tracking and stability with measured acoustic paths from a real-time system.

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Frequency domain analysis of the mirror‐modified filtered‐x least mean squares algorithm with low ambient noise

Lopes

Gerald

2021

Adaptive Control & Signal

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Summary The mirror‐modified filtered‐x least mean squares (MMFxLMS ) algorithm is a variation of the FxLMS algorithm with online secondary path modeling that cannot diverge due to secondary path modeling errors. However, problems may occur when the ambient noise is not limited due to insufficient modeling power. This work shows that under a frequency domain analysis without ambient noise, the MMFxLMS algorithm is always stable, and expressions for the maximum residual noise level at any given time are obtained. It is also shown that, under the same context, convergence to the minimum residual noise is guaranteed. Still, convergence can be much slower for high secondary path modeling errors than that of the LMS or MFxLMS algorithms. Simulations confirm these results.

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Auxiliary Noise Power Scheduling Algorithm for Active Noise Control with On-line Secondary Path Modelling and Sudden Changes

Cited by 21 publications

References 18 publications

A narrowband active noise control system with reference synthesis

A narrowband active noise control system with reference synthesis

Time-domain Kalman filter for active noise cancellation headphones

Frequency domain analysis of the mirror‐modified filtered‐x least mean squares algorithm with low ambient noise

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