A computationally efficient frequency-domain LMS algorithm with constraints on the adaptive filter

Rafaely, Boaz; Elliot, S.J.

doi:10.1109/78.845922

Cited by 65 publications

(38 citation statements)

References 6 publications

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“…The subband convergence gains are presented against frequency in Figure 12, and it is clear from this plot that the subband implementation allows a significant range of convergence gains to be selected over the control bandwidth. The full-band implementation, however, only allows a single convergence gain to be selected and this is limited by the maximum eigenvalue of the full-band Hessian matrix as described by (13). The spectrum of the cost function, given by the sum of the squared error signals, before control and after the three control algorithms have been adapting for 20 seconds is shown in Figure 13.…”

Section: Direct Comparison Of the Two Subband Implementationsmentioning

confidence: 99%

“…In general, these methods achieve a reduction in the computational demand by converting the time domain convolution into a frequency domain multiplication, and under certain conditions can allow frequency dependent convergence gains [12]. Due to the block-based processing of frequency domain adaptive algorithms, a delay is introduced into the update of the control filter coefficients [13] and this can limit performance. An alternative method of implementing the adaptive control algorithm in frequency bands was proposed by Morgan and Thi [15] and is referred to as the delayless subband adaptive filtering architecture.…”

Section: Introductionmentioning

confidence: 99%

“…An alternative approach to potentially improving both the convergence speed and computational efficiency of active noise control systems is to employ a frequency domain processing strategy [12], [13], [14]. In general, these methods achieve a reduction in the computational demand by converting the time domain convolution into a frequency domain multiplication, and under certain conditions can allow frequency dependent convergence gains [12].…”

Section: Introductionmentioning

confidence: 99%

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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: Direct Comparison Of the Two Subband Implementationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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 some applications, it is required to limit the maximum output level to prevent overdriving the transducer, or to maintain a specified system power budget. In a frequency-domain implementation of the least-mean-square (LMS) algorithm, the limiting constraints can be placed directly in the frequency domain, allowing the adaptive filter response to be reduced in the frequency regions of constraint violation, with minimal effect at other frequencies [2]. Constraints can be placed on either the filter gain, or filter output power, as appropriate for the application.…”

Section: Introductionmentioning

confidence: 99%

“…Adding gain constraints to the adaptive filter prevents distortion at those frequencies by limiting the peak magnitude of the filter coefficients [2]. Applications of powerconstrained adaptive systems include requirements to limit the maximum power delivered to S(z) to a predetermined constraint value to prevent overdriving the transducer, prevent output amplifier saturation, or prevent other nonlinear behavior [4].…”

Section: Introductionmentioning

confidence: 99%

An active noise control algorithm with gain and power constraints on the adaptive filter

Kozacky

Ogunfunmi

2013

EURASIP J. Adv. Signal Process.

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This article develops a new adaptive filter algorithm intended for use in active noise control systems where it is required to place gain or power constraints on the filter output to prevent overdriving the transducer, or to maintain a specified system power budget. When the frequency-domain version of the least-mean-square algorithm is used for the adaptive filter, this limiting can be done directly in the frequency domain, allowing the adaptive filter response to be reduced in frequency regions of constraint violation, with minimal effect at other frequencies. We present the development of a new adaptive filter algorithm that uses a penalty function formulation to place multiple constraints on the filter directly in the frequency domain. The new algorithm performs better than existing ones in terms of improved convergence rate and frequency-selective limiting.

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Frequency‐domain integration of acoustic echo cancellation and a generalized sidelobe canceller with improved robustness

Herbordt

Kellermann

2002

Trans Emerging Tel Tech

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Abstract. For hands-free acoustic humadmachine interfaces, as required, e.8.. for automatic speech recognition, teleconferencing, and other multimedia services. microphone arrays using generalized sidelobe cancellers (GSCs) in conjunction with acoustic echo cancellation (AEC) can be efficiently applied for optimum communication. This contribution first devises a new structure for combining AEC and GSC in the frequency domain. We show that computational complexity is reduced by more than a factor of ten compared to a time-domain mngement. Second, robustness issues of the GSC adaptation mechanism are addressed. We illustrate how robust adaptation is assured in the new structure. Third, we propose alternatives for the fixed beamformer part of the GSC, which allow to specify (a) the width of the target tracking region and (b) improve the suppression of low frequencies. GSC robustness constraints are included using linear optimization with configurable penalty functions.

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A computationally efficient frequency-domain LMS algorithm with constraints on the adaptive filter

Cited by 65 publications

References 6 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

An active noise control algorithm with gain and power constraints on the adaptive filter

Frequency‐domain integration of acoustic echo cancellation and a generalized sidelobe canceller with improved robustness

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