Fast Affine Projection Algorithms for Filtered-x Multichannel Active Noise Control

Ferrer, Miguel; González, Alberto; Diego, María de; Piñero, Gema

doi:10.1109/tasl.2008.2004295

Cited by 47 publications

(20 citation statements)

References 17 publications

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“…Improvements in the convergence speed without the computational demands of the RLS algorithm have been achieved using hybrid algorithms that combine the LMS and RLS adaptation methods [9], as well as through the use of affine projection based algorithms [10]. The computational problem associated with large-scale active noise control systems has also recently been addressed by exploiting the parallel processing provided by graphics processing units [11].…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Delayless Subband Adaptive Filtering for Multi-Input Multi-Output Active Noise Control Applications

Cheer

Daley

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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The broadband control of noise and vibration using multi-input, multi-output (MIMO) active control systems has a potentially wide variety of applications. However, the performance of MIMO systems is often limited in practice by high computational demand and slow convergence speeds. In the somewhat simpler context of single-input, singleoutput broadband control, these problems have been overcome through a variety of methods including subband adaptive filtering. This paper presents an extension of the subband adaptive filtering technique to the MIMO active control problem and presents a comprehensive study of both the computational requirements and control performance.The implementation of the MIMO filtered-x LMS algorithm using subband adaptive filtering is described and the details of two specific implementations are presented. The computational demands of the two MIMO subband active control algorithms are then compared to that of the standard full-band algorithm. This comparison shows that as the number of subbands employed in the subband algorithms is increased, the computational demand is significantly reduced compared to the full-band implementation provided that a restructured analysis filter-bank is employed. An analysis of the convergence of the MIMO subband adaptive algorithm is then presented and this demonstrates that although the convergence of the control filter coefficients is dependent on the eigenvalue spread of the subband Hessian matrix, which reduces as the number of subbands is increased, the convergence of the cost function is limited for large numbers of subbands due to the simultaneous increase in the weight stacking distortion. The performance of the two MIMO subband algorithms and the standard full-band algorithm has then been assessed through a series of time-domain simulations of a practical active control system and it has been shown that the subband algorithms are able to achieve a significant increase in the convergence speed compared to the full-band implementation. Index TermsActive noise control, Active vibration control, Delayless subband adaptive filtering, MIMO J. Cheer is with the Institute

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Section: Introductionmentioning

confidence: 99%

An Investigation of Delayless Subband Adaptive Filtering for Multi-Input Multi-Output Active Noise Control Applications

Cheer

Daley

2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…In [17], efficient versions of the FAP algorithm that incur low computational loads are developed. However, the recursive approach used for matrix inversion does not present any improvement over the original version for low projection orders and adaptive filters with few coefficients, and therefore, it is a good option only for long-length filters.…”

Section: Introductionmentioning

confidence: 99%

“…Such modifications result in better final errors in the steady state, but the computational cost remains the same; furthermore, most of these proposals are intended for acoustic echo canceler applications [17][18][19][20][21].…”

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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“…This in turn restricts the efficacy of ANC to frequencies less than half of its sampling frequency. A number of researchers have shown interest in developing computationally efficient algorithms, otherwise known as fast ANC algorithms [10][11][12][13][14][15][16][17][18]. A class of fast ANC algorithms known as the block algorithm, which exploits frequency-domain convolution and correlation operations, has been shown to achieve a significant computational advantage over its conventional time-domain counterpart [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A computationally efficient frequency-domain filtered-X LMS algorithm for virtual microphone

Das

Moreau

Cazzolato

2013

Mechanical Systems and Signal Processing

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The computational complexity of the virtual FXLMS algorithm is higher than that of the conventional FXLMS algorithm. The additional complexity comes from computation of three secondary path transfer functions (as opposed to one) and a transfer function between the physical and the virtual microphones. The order of these transfer functions may be very high in practical situations where the acoustic damping is low. The high computational complexity of the virtual FXLMS algorithm imposes issues like high power consumption, making it difficult to implement the algorithm in battery operated ANC devices such as active headsets. In addition, the operating sampling frequency of the algorithm is limited and this in turn restricts its operation to relatively low frequency applications. In this paper, a new virtual FXLMS algorithm is derived by implementing all of the secondary path transfer functions in the frequency domain. The algorithm is simulated using measured transfer functions in a duct with low acoustic damping. Implementation schemes are proposed for the new frequency-domain virtual FXLMS algorithm, citing its advantages for use as an efficient real-time active noise control algorithm.Index Terms: Active noise control, adaptive filter, frequency-domain adaptive filter, computational complexity, virtual ANC. 2

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Fast Affine Projection Algorithms for Filtered-x Multichannel Active Noise Control

Cited by 47 publications

References 17 publications

An Investigation of Delayless Subband Adaptive Filtering for Multi-Input Multi-Output Active Noise Control Applications

An Investigation of Delayless Subband Adaptive Filtering for Multi-Input Multi-Output Active Noise Control Applications

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

A computationally efficient frequency-domain filtered-X LMS algorithm for virtual microphone

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