On the convergence of real-time active noise control systems

Ardekani, Iman Tabatabaei; Abdulla, Waleed H.

doi:10.1016/j.sigpro.2010.12.012

Cited by 50 publications

(17 citation statements)

References 9 publications

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“…It is known that FPGA has the capability of processing a signal in a parallel manner hence allowing a complete computation of weight updates within a few clock cycles. The experimental work by the author [13] also further verify the theoretical principle established from previous studies [14]- [17], that are initially based on simplifying assumptions.…”

Section: Introductionsupporting

confidence: 75%

“…Therefore, in general the error signal will converge much slower. To overcome this, an analysis on the LMS convergence behaviour has been carried out using field programmable gate arrays (FPGA) on ANC system for duct application [13]. It is known that FPGA has the capability of processing a signal in a parallel manner hence allowing a complete computation of weight updates within a few clock cycles.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, motivated by the idea presented in [13] we utilize the FPGA advantage to achieve real-time and faster convergence of error signal for LMS-based algorithm. A real-time hardware simulation is carried out to implement the modified Filtered-X LMS algorithm namely MFxLMS.…”

Section: Introductionmentioning

confidence: 99%

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Multiple Iteration of Weight Updates for Least Mean Square Adaptive Filter in Active Noise Control Application

et al. 2017

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Abstract. The method of least mean square (LMS) is the commonly used algorithm in Adaptive filter due to its simplicity and robustness in implementation. In Active Noise Control application, a filtered reference signal is used prior to LMS algorithm to overcome the constraint on stability and convergence performance of the system due to the existence of the auxiliary path. This is known as Filtered-X LMS algorithm. In conventional Filtered-X LMS algorithm, each filter weight is updated once on every audio sample. This paper proposes the improved version of Filtered-X LMS algorithm with the use of multiple iteration of filter weight on every sample of audio signal. The proposed work uses field programmable gate arrays to realize real-time simulation on hardware for the noise signal of 500 Hz. Results from the real-time hardware simulations have shown much faster error convergence and better adaptation performance for different selections of learning constant μ, as compared with the conventional method.

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Section: Introductionsupporting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

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Multiple Iteration of Weight Updates for Least Mean Square Adaptive Filter in Active Noise Control Application

et al. 2017

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“…The principal method is to use the independence assumption [8], [9], [10]. The FXLMS algorithm has also been analyzed on the basis of the independence assumption [11], [12], [13], [14]. In this assumption, successive input vectors of the tapped-delay line are assumed to be independently generated at each time step.…”

Section: Introductionmentioning

confidence: 99%

A theory of the FXLMS algorithm based on statistical-mechanical method

Miyoshi

Kajikawa

2013

2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA)

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We analyze the learning curves of the FXLMS algorithm using a statistical-mechanical method. Cross-correlations between the element of a primary path and that of an adaptive filter and autocorrelations of the elements of the adaptive filter are treated as macroscopic variables. We obtain simultaneous differential equations that describe the dynamical behaviors of the macroscopic variables under the conditions in which the tapped-delay line is long. We analytically solve the equations to obtain the correlations and finally compute the mean-square error. Introducing the correlation function of the input signal, the theory can treat not only the white but also the nonwhite signal. The obtained theory quantitatively agrees with the results of computer simulations.

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“…These algorithms perform two tasks: i) computing y (n) as the response of an adaptive filter, W , to x (n) and ii) updating the weight vector of W , w (n), by using an update equation, e.g. the FxLMS update equation [15] given by w (n + 1) = w (n) + μe (n) s (n) ⊗ x (n) (1) where ⊗ denotes the linear convolution, μ is the step-size and s (n) is the impulse response of S. ANC systems can attenuate the noise propagating in a duct by about −20dB in practice [1], [3], [7], [9]. However, they cannot significantly attenuate the noise propagating in free space.…”

Section: Introductionmentioning

confidence: 99%

Adaptive active noise control in free space

Ardekani

Abdulla

2013

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

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This paper develops a reliable methodology for active control of acoustic noise in free space. The core of this paper consists of a root locus analysis on the adaptation process performed in active noise control. Based on this analysis, a novel active noise control algorithm is derived. This algorithm is fully implemented in an experimental setup. Different experiments with this setup show that the traditional active noise control algorithm is not stable when the setup is used in free space. However, the proposed algorithm is stable and converges at a high convergence rate until the noise level is reduced by 15 dB in steady-state conditions.

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On the convergence of real-time active noise control systems

Cited by 50 publications

References 9 publications

Multiple Iteration of Weight Updates for Least Mean Square Adaptive Filter in Active Noise Control Application

Multiple Iteration of Weight Updates for Least Mean Square Adaptive Filter in Active Noise Control Application

A theory of the FXLMS algorithm based on statistical-mechanical method

Adaptive active noise control in free space

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