Active noise control with on-line estimation of non-Gaussian noise characteristics (original) (raw)
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Active Control of Impulsive Noise with On-Line Outlier Detection
Proceedings of the 18th IFAC World Congress, 2011
Conventional active noise control algorithms may fail to achieve convergence in the presence of non-Gaussian or impulsive noise. Typical workarounds for this problem are based on the concept of perturbing as little as possible the filter weight update process by discarding or discounting samples associated with outliers. The effectiveness of these methods depends on how accurately outliers can be detected while maintaining a sufficiently low algorithm complexity. A simple on-line recursive procedure is here suggested that reliably estimates amplitude thresholds for outlier detection. The resulting adaptive scheme for noise control can be shown to outperform existing methods, both for invariant and time-varying noise distributions.
Active noise control of impulsive noise with selective outlier elimination
2013 American Control Conference, 2013
Traditional active noise control (ANC) methods are based on adaptive filtering algorithms designed to minimize the noise variance. The convergence of such algorithms may be jeopardized in the presence of non-Gaussian noise signals, characterized by a marked impulsiveness (and infinite secondorder moments), such as are frequently encountered in realworld acoustic settings. ANC methods have been recently extended to deal with such signals, modifying the weight update of the adaptive filter so that out-of-range samples are discarded or discounted. These methods require precise a priori knowledge of the impulsive characteristics of the noise and are generally not suitable for signals where such characteristics are timevarying. This work introduces an algorithm, based on an adaptive box-plot approach for outlier detection, which does not rely on any a priori information and yields uniformly high attenuation performance in all conditions tested in simulation.
Robust adaptive algorithm for active noise control of impulsive noise
2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009
The paper concerns active control of impulsive noise. The most famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems is based on the minimization of variance of error signal. The impulsive noise can be modeled using non-Gaussian stable process for which second order moments do not exist. The FxLMS algorithm, therefore, becomes unstable for the impulsive noise. Among the existing algorithms for ANC of impulsive noise, one is based on the minimizing least mean p-power (LMP) of the error signal, resulting in FxLMP algorithm. The other is based on modifying; on the basis of statistical properties; the reference signal in the update equation of the FxLMS algorithm. In this paper, the proposed algorithm is a modification and combination of these two approaches. Extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithm. It achieves the best performance among the existing algorithms, and at the same computational complexity as that of FxLMP algorithm.
Improved adaptive algorithm for active noise control of impulsive noise
2008 9th International Conference on Signal Processing, 2008
The paper concerns active control of impulsive noise. The most famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems is based on the minimization of variance of mean-squared-error signal. The impulsive noise can be modeled using non-Gaussian stable process for which second order moments do not exist. The FxLMS algorithm, therefore, becomes unstable for the impulsive noise. Among the existing algorithms for ANC of impulsive noise, one is based on the minimizing least mean p-power (LMP) of the error signal, resulting in FxLMP algorithm. The other is based on modifying; on the basis of statistics properties; the reference signal in the update equation of the FxLMS algorithm. In this paper, the proposed algorithm is an extension of the later approach. Extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithm. It achieves the best performance among the existing algorithms, and at the same computational complexity as that of FxLMS algorithm.
Adaptive algorithms for active noise control of SαS impulse noise
2008 International Symposium on Intelligent Signal Processing and Communications Systems, 2009
This paper concerns active noise control (ANC) of impulsive noise modeled using Gaussian stable processes. The most famous filtered-x least mean square (FxLMS) algorithm for ANC systems is based on minimization of variance of mean squared error signal. For the impulse noise, the FxLMS algorithm becomes unstable, as second order moments do not exist for Gaussian stable processes. Among the existing algorithms for ANC of impulsive noise, one is based on minimizing least mean ppower (LMP) of the error signal, resulting in FxLMP algorithm. The other is based on modifying the reference signal in update of FxLMS algorithm, on the basis of statistics of the reference signal. In this paper, the proposed algorithm is an extension of the later approach. Extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithm. It achieves the best performance among the existing algorithms, and at the same computational complexity as that of FxLMS algorithm.
Applied Acoustics, 2011
The paper concerns active control of impulsive noise having peaky distribution with heavy tail. Such impulsive noise can be modeled using non-Gaussian stable process for which second order moments do not exist. The most famous filtered-x least mean square (FxLMS) algorithm for active noise control (ANC) systems is based on the minimization of variance (second order moment) of error signal, and hence, becomes unstable for the impulsive noise. In order to improve the robustness of adaptive algorithms for processes having distributions with heavy tails (i.e. signals with outliers), either (1) a robust optimization criterion may be used to derive the adaptive algorithm or (2) the large amplitude samples may be ignored or replaced by an appropriate threshold value. Among the existing algorithms for ANC of impulsive noise, one is based on the minimizing least mean p-power (LMP) of the error signal, resulting in FxLMP algorithm (approach 1). The other is based on modifying; on the basis of statistical properties; the reference signal in the update equation of the FxLMS algorithm (approach 2). In this paper we propose two solutions to improve the robustness of the FxLMP algorithm. In first proposed algorithm, the reference and the error signals are thresholded before being used in the update equation of FxLMP algorithm. As another solution to improve the performance of FxLMP algorithm, a modified normalized step size is proposed. The computer simulations are carried out, which demonstrate the effectiveness of the proposed algorithms.
Less complex solutions for active noise control of impulsive noise
Analog Integrated Circuits and Signal Processing, 2019
All adaptive algorithms suffer stability issues when employed for the impulsive noise control under the domain of active noise control (ANC) systems. There is a dire need of investigations to overcome this limitation for the impulsive noise, a robust adaptive algorithm is proposed in literature. In the first part of paper, this robust adaptive algorithm is tested for the first time under ANC environment for impulsive noise cancellation and thus, a new ANC algorithm named filtered-x least cosine hyperbolic (FxLCH) algorithm is presented. Simulations are carried out to validate the improved performance of proposed FxLCH algorithm where the impulsive noise realizations are generated by symmetric a-stable distributions. Moreover, the proposed solutions perform better than the standard filtered-x least mean square (FxLMS) algorithm including its variants, and it shows better stability and converges faster than its competitors. Robustness of the algorithm is a constraint in the presence of high impulsive noise. To overcome this problem and to enhance the robustness of proposed FxLCH algorithm, two modifications are suggested. First proposed modification clips the reference and error signals (CFxLCH algorithm), while the second modification integrates already reported normalized step size with FxLCH (MFxLCH) algorithm. The performance of suggested MFxLCH algorithm is validated by extensive simulations. The results exhibited that MFxLCH algorithm acts as a trade-off between FxLMS and filtered-x recursive least square (FxRLS) family algorithms. It has shown better convergence speed than that of FxLMS family algorithms and can approach steady state error as of FxRLS family with almost same computational complexity as of FxLMS family algorithms.
Adaptive robust impulse noise filtering
IEEE Transactions on Signal Processing, 1995
It is well known that when data is contaminated by non-Gaussian noise, conventional linear systems may perform poorly. This paper presents an adaptive robust filter (adaptive preprocessor) for canceling impulsive components when the nominal process (or background noise) is a correlated, possibly nonstationary, Gaussian process. The proposed preprocessor does not require iterative and/or batch processing or prior knowledge about the nominal Gaussian process; consequently, it can be implemented in real time and adapt to changes in the environment. Based on simulation results, the proposed adaptive preprocessor shows superior performances over presently available techniques for cleaning impulse noise. Using the proposed adaptive preprocessor to clean the impulsive components in received data samples, conventional linear systems based on the Gaussian assumption can work in an impulsive environment with little if any modification. The technique is applicable to a wide range of problems, such as detection, power spectral estimation, and jamming or clutter suppression in impulsive environments.
Applied Acoustics, 2015
This paper deals with the adaptive algorithms for active noise control (ANC) systems being employed for the impulsive noise sources. The standard filtered-x least mean square (FxLMS) algorithm; based on the minimization of the variance of the error signal; is well suited for attenuation of Gaussian noise sources. For the impulsive noise; modeled as a stable non-Gaussian process; however, the second order moments do not exist and hence the FxLMS algorithm becomes unstable. The filtered-x least mean p-power (FxLMP) algorithm-based on minimizing the fractional lower order moment (FLOM)-gives robust performance for impulsive ANC; however, its convergence speed is very slow. This paper proposes two data-reusing (DR)-based adaptive algorithms for impulsive ANC. The Proposed-I DR algorithm is based on the normalized step-size FxLMS (NSS-FxLMS) algorithm, and the Proposed-II DR algorithm is based on the Author's recently proposed NSS generalized FxLMP (NSS-GFxLMP) algorithm. Extensive simulations are carried out, which demonstrate the effectiveness of the proposed algorithms in comparison with the existing algorithms.
New FxLMAT-Based Algorithms for Active Control of Impulsive Noise
IEEE Access
In the presence of non-Gaussian impulsive noise (IN) with a heavy tail, active noise control (ANC) algorithms often encounter stability problems. While adaptive filters based on the higher-order error power principle have shown improved filtering capability compared to the least mean square family algorithms for IN, however, the performance of the filtered-x least mean absolute third (FxLMAT) algorithm tends to degrade under high impulses. To address this issue, this paper proposes three modifications to enhance the performance of the FxLMAT algorithm for IN. To improve stability, the first alteration i.e. variable step size FxLMAT (VSSFxLMAT)algorithm is suggested that incorporates the energy of input and error signal but has slow convergence. To improve its convergence, the second modification i.e. filtered x robust normalized least mean absolute third (FxRNLMAT) algorithm is presented but still lacks robustness. Therefore, a third modification i.e. modified filtered-x RNLMAT (MFxRNLMAT) is devised, which is relatively stable when encountered with high impulsive noise. With comparable computational complexity, the proposed MFxRNLMAT algorithm gives better robustness and convergence speed than all variants of the filtered-x least cos hyperbolic algorithm, and filtered-x least mean square algorithm. INDEX TERMS Adaptive signal processing, non-Gaussian, mean noise reduction.