The frequency partitioned block modified filtered-x NLMS with orthogonal correction factors for multichannel Active Noise Control

The Normalized Least Mean Square (NLMS) algorithm with a filtered-x structure (FxNLMS) is a widely used adaptive algorithm for Active Noise Control (ANC) due to its simplicity and ease of implementation. One of the major drawbacks is its slow convergence. A modified filtered-x structure (MFxNLMS) can be used to moderately improve the speed of convergence, but it does not offer a huge improvement. A greater increase in the speed of convergence can be obtained by using the MFxNLMS algorithm with orthogonal correction factors (M-OCF), but the usage of orthogonal correction factors also increases the computational complexity and limits the usage of the M-OCF in massive real-time applications. However, Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform to ameliorate this computational drawback. In this paper, we propose to derive the M-OCF algorithm to a partitioned block-based version in the frequency domain (FPM-OCF) for multichannel ANC systems in order to better exploit the parallel capabilities of the GPUs. The results show improvements in the convergence rate of the FPM-OCF algorithm in comparison to other NLMS-type algorithms and the usefulness of GPU devices for developing versatile, scalable, and low-cost multichannel ANC systems.     

Binormalized data-reusing adaptive filtering algorithm for active control of impulsive sources

The main objective of active noise control (ANC) is to provide attenuation for the environmental acoustic noise. The adaptive algorithms for ANC systems work well to attenuate the Gaussian noise; however, their performance may degrade for non-Gaussian impulsive noise sources. Recently, we have proposed variants of the most famous ANC algorithm, the filtered-x least mean square (FxLMS) algorithm, where an improved performance has been realized by thresholding the input data or by efficiently normalizing the step-size. In this paper, we propose a modified binormalized data-reusing (BNDR)-based adaptive algorithm for impulsive ANC. The proposed algorithm is derived by minimizing a modified cost function, and is based on reusing the past and present samples of data. The main contribution of the paper is to develop a practical DR-type adaptive algorithm, which incorporates an efficiently normalized step-size, and is well suited for ANC of impulsive noise sources. The computer simulations are carried out to demonstrate the effectiveness of the proposed algorithm. It is shown that an improved performance has been realized with a reasonable increase in the computational complexity.     

New IIR filter-based adaptive algorithm in active noise control applications: Commutation error-introduced LMS algorithm and associated convergence assessment by a deterministic approach

In this study, commutation error (CE) is defined in adaptive infinite impulse response (IIR) filter-based ANC systems. CE is subsequently introduced into a new residual error to develop a new LMS-based ANC algorithm in an aim to liberate the restriction of slow adaptation posed on traditional ANC algorithms. A new deterministic analysis based on a linear time-varying system is performed to investigate convergence properties of the developed algorithm: (1) An optimal step size for the fastest convergence rate can be derived. (2) Given a persistent excitation condition and a step-size constraint, we find that the algorithm is uniformly asymptotically stable. Computer simulations indeed demonstrate a greatly improved convergence rate and efficient ANC performance for the developed algorithm as compared with that using the conventional algorithms. Experimental results verify the enhanced ANC performance in real applications. These together support the new IIR filter-based adaptive algorithm that includes CE for superior ANC performance with respect to the convergence rate and noise reduction level.     

An affine projection algorithm with variable step size and projection order

It is known that the performance of adaptive algorithms is constrained by their computational cost. Thus, affine projection adaptive algorithms achieve higher convergence speed when the projection order increases, which is at the expense of a higher computational cost. However, regardless of computational cost, a high projection order also leads to higher final error at steady state. For this reason it seems advisable to reduce the computational cost of the algorithm when high convergence speed is not needed (steady state) and to maintain or increase this cost only when the algorithm is in transient state to encourage rapid transit to the permanent regime. The adaptive order affine projection algorithm presented here addresses this subject. This algorithm adapts its projection order and step size depending on its convergence state by simple and meaningful rules. Thus it achieves good convergence behavior at every convergence state and very low computational cost at steady state.     

Particle swarm optimization based nonlinear active noise control under saturation nonlinearity

In this paper, a method is proposed to overcome the saturation non-linearity linked to the microphones and loudspeakers of active noise control (ANC) system. The reference microphone gets saturated when the acoustic noise at the source increases beyond the dynamic limits of the microphone. When the controller tries to drive the loudspeaker system beyond its dynamic limits, the saturation nonlinearity is also introduced into the system. The secondary path which is generally estimated with a low level auxiliary noise by a linear transfer function does not model such saturation nonlinearity. Therefore, the filtered-x least mean square (FXLMS) algorithm fails to perform when the noise level is increased. For alleviating the saturation nonlinearity effect a nonlinear functional expansion based ANC algorithm is proposed where the particle swarm optimization (PSO) algorithm is suitably applied to tune the parameters of a filter bank based functional link artificial neural network (FLANN) structure, named as PSO based nonlinear structure (PSO-NLS) algorithm. The proposed algorithm does not require any computation of secondary path estimate filtering unlike other conventional gradient based algorithms and hence has got computational advantage. The computer simulation experiments show its superior performance compared to the FXLMS, filtered-s LMS and genetic algorithms under saturation present at both at secondary and reference paths. The paper also includes a sensitivity analysis to study the effect of different parameters on ANC performance.     

新的基于仿射投影算法的盲多用户检测器*

多径衰落信道下的盲自适应多用户检测算法的运算复杂度通常都比较大,将基于仿射投影算法的盲多用户检测器与RAKE分集技术相结合,提出了一种盲自适应接收算法。该接收算法平衡了收敛速度和计算复杂度之间的矛盾,具有相对较好的收敛性能及较小的运算复杂度。通过模拟实验比较了几种算法的收敛、跟踪及误码性能,结果表明该方法具有明显的整体优势。     

Adaptive Parallel Quadratic-Metric Projection Algorithms

This paper indicates that an appropriate design of metric leads to significant improvements in the adaptive projected subgradient method (APSM), which unifies a wide range of projection-based algorithms [including normalized least mean square (NLMS) and affine projection algorithm (APA)]. The key is to incorporate a priori (or a posteriori) information on characteristics of an estimandum, a system to be estimated, into the metric design. We propose a family of efficient adaptive filtering algorithms based on a parallel use of quadratic-metric projection, which assigns every point to the nearest point in a closed convex set in a quadratic-metric sense. We present two versions: (1) constant-metric and (2) variable-metric, i.e., the metric function employed is (1) constant and (2) variable among iterations. As a constant-metric version, adaptive parallel quadratic-metric projection (APQP) and adaptive parallel min-max quadratic-metric projection (APMQP) algorithms are naturally derived by APSM, being endowed with desirable properties such as convergence to a point optimal in asymptotic sense. As a variable-metric version, adaptive parallel variable-metric projection (APVP) algorithm is derived by a generalized APSM, enjoying an extended monotone property at each iteration. By employing a simple quadratic-metric, the computational complexity of the proposed algorithms is kept linear with respect to the filter length. Numerical examples demonstrate the remarkable advantages of the proposed algorithms in an application to acoustic echo cancellation.     

New recursive-least-squares algorithms for nonlinear active controlof sound and vibration using neural networks

In recent years, a few articles describing the use of neural networks for nonlinear active control of sound and vibration were published. Using a control structure with two multilayer feedforward neural networks (one as a nonlinear controller and one as a nonlinear plant model), steepest descent algorithms based on two distinct gradient approaches were introduced for the training of the controller network. The two gradient approaches were sometimes called the filtered-x approach and the adjoint approach. Some recursive-least-squares algorithms were also introduced, using the adjoint approach. In this paper, an heuristic procedure is introduced for the development of recursive-least-squares algorithms based on the filtered-x and the adjoint gradient approaches. This leads to the development of new recursive-least-squares algorithms for the training of the controller neural network in the two networks structure. These new algorithms produce a better convergence performance than previously published algorithms. Differences in the performance of algorithms using the filtered-x and the adjoint gradient approaches are discussed in the paper. The computational load of the algorithms discussed in the paper is evaluated for multichannel systems of nonlinear active control. Simulation results are presented to compare the convergence performance of the algorithms, showing the convergence gain provided by the new algorithms.     

A robust evolutionary feedforward active noise control system using Wilcoxon norm and particle swarm optimization algorithm

The conventional filtered-x least mean square (FxLMS) algorithm commonly employed for active noise control (ANC) is sensitive to disturbances acquired by the error microphone and yields poor performance in such scenario. To circumvent this problem, in this paper, a Wilcoxon FxLMS (WFxLMS) algorithm is proposed and used in the design of an efficient ANC which is robust to outliers in the secondary path and immune to burst noise acquired by the error microphone. It is demonstrated through simulation study that under such situation the proposed algorithm outperforms the traditional FxLMS algorithm. A particle swarm optimization (PSO) algorithm based robust ANC system, which does not require the modeling of the secondary path is also derived in the paper. Improved performance of the robust evolutionary ANC system over L2 norm based evolutionary ANC system is also shown.     

Selective-Tap Adaptive Filtering With Performance Analysis for Identification of Time-Varying Systems

Selective-tap algorithms employing the MMax tap selection criterion were originally proposed for low-complexity adaptive filtering. The concept has recently been extended to multichannel adaptive filtering and applied to stereophonic acoustic echo cancellation. This paper first briefly reviews least mean square versions of MMax selective-tap adaptive filtering and then introduces new recursive least squares and affine projection MMax algorithms. We subsequently formulate an analysis of the MMax algorithms for time-varying system identification by modeling the unknown system using a modified Markov process. Analytical results are derived for the tracking performance of MMax selective tap algorithms for normalized least mean square, recursive least squares, and affine projection algorithms. Simulation results are shown to verify the analysis.     

Improved performance of impulse active noise control using active function threshold with absolute harmonic variable step-size algorithm

The Standard (conventional) adaptive algorithms exhibits low convergence rate and minimum noise suppression, or else the system becomes unstable under Gaussian and non-Gaussian (impulsive noise SαS distributions) noise environments. In order to overcome the drawback of traditional algorithms (i.e., to eliminate unwanted noise), the popular algorithm Filtered Cross Minimum Square (FxLMS) is used in Active Noise Control (ANC), not only to improve its efficiency but also to improve its performance. In this paper, we proposed two improvements: first, we proposed a novel method Active threshold function FxLMS (ATFxLMS) being employed in ANC in the paths of primary (reference) and error signals; a second proposal is employing the Variable Step-Size based on Absolute Harmonic Mean (AHMVSS) of error signal. The idea behind this method is that the step-size of the algorithm varies depending on the harmonic mean of error signal obtained from the error location. In comparison to the fixed step-size algorithm, the proposed ATF-AHMVSS provided an improved convergence rate for the desired ANC efficiency. Moreover computational complication of the proposed method was examined as it was found that the proposed algorithm provided stable condition for ANC systems. Computer simulation results are revealed that the proposed (AT & AHMVSS-FxLMS) algorithm have attained excellent performance in terms of convergence speed, noise reduction and minimum steady state error as compared to other existing algorithms under different noise inputs. The results obtained from the proposed algorithm show outperformance compared to traditional adaptive algorithms.     

New FIR filter-based adaptive algorithms incorporating with commutation error to improve active noise control performance

This study considers a commutation error (CE) that results from a difference associated with the altered sequence in real active noise control (ANC) applications as compared with that at the derivation stage. New adaptive algorithms are developed as FxLMS/CE, FxNLMS/CE and FxRLS/CE in an aim to eliminate the CE-associated disturbance and to liberate the restriction of slow adaptation imposed on the existing adaptive algorithms in the ANC applications. Computer simulations show that the rate of convergence is greatly improved for the new adaptive algorithms as compared with that of the conventional algorithms. In parallel with the improved rate of convergence, simulations exhibit efficient ANC performance for all CE-based algorithms. The best ANC performance is seen for FxRLS/CE algorithm that can acquire of convergence rate and reduction of sound pressure level for band-limited white noise. All experimental results indeed demonstrate enhanced ANC performance; the FxNLMS/CE algorithm can acquire of convergence rate and reduction of sound pressure level for band-limited white noise. Our data together support the effectiveness to include CE into the FIR filter-based adaptive algorithms for superior ANC performance with respect to the convergence speed and noise reduction level.     

基于微分麦克风阵列的自适应语音增强算法研究及DSP实现

自适应滤波是语音增强算法中的常用技术, 而算法复杂度与收敛速度是设计各种自适应算法需要首要考虑的问题. 本文提出一种用于片上的语音增强自适应滤波新算法. 该算法分两步实现, 首先, 利用一阶微分麦克风阵列, 获得噪声的实时估计; 其次, 对传统的仿射投影算法(Affine projection algorithm, APA)加以改进, 得到计算误差向量的快速算法, 并根据估计误差动态调整搜索步长以及仿射投影维数, 对带噪语音进行自适应滤波消噪. 在TMS320VC5509 DSP芯片上实现该算法. 实验表明, 算法的自适应滤波过程具有接近递推最小二乘算法(Recursive least squares, RLS)的快速收敛速度, 以及类似最小均方误差算法(Least mean squares, LMS)的低算法复杂度.     

一种新的变步长比例仿射投影算法研究

研究比例仿射投影算法,针对自适应算法收敛速度和稳态误差之间的矛盾,提出了一种变步长的改进比例仿射投影算法( VSS- IPAPA).利用后验误差去补偿干扰信号对系统稳态性能的影响,得到了算法新的最优步长准则,根据步长准则以及先验误差与后验误差之间的联系,导出了一种适用于比例仿射投影的步长调节方法.综合了稀疏算法、数据重用方法及变步长的优点.最后通过对改进算法进行仿真,结果表明,在增加少量计算量的情况下,系统的收敛速度和稳态性能有明显的改善,证明了比例仿射投影算法的有效性.     

一种新的变步长自适应滤波算法研究

针对滤波器在亚模型(under-modeling)工作状态下定步长自适应算法收敛速度和稳态误差之间的矛盾,提出一种变步长分割式比例仿射投影算法(VSS-SPAPA)。该算法考虑到系统干扰噪声和滤波器权系数个数小于回声路径长度时引起的亚模型噪声对回声消除系统性能的影响,利用后验误差去补偿这两类噪声的负面作用,建立一个新的目标函数,根据该目标函数,导出一种适用于比例仿射投影算法整体步长的调节方法。仿真结果表明:在增加少量计算量的情况下,新算法的收敛速度和稳态性能与定步长比例仿射投影算法以及已有变步长算法相比得到了明显提高。     

Improved functional link artificial neural network via convex combination for nonlinear active noise control

A method relying on the convex combination of two normalized filtered-s least mean square algorithms (CNFSLMS) is presented for nonlinear active noise control (ANC) systems with a linear secondary path (LSP) and nonlinear secondary path (NSP) in this paper. The proposed CNFSLMS algorithm-based functional link artificial neural network (FLANN) filter, aiming to overcome the compromise between convergence speed and steady state mean square error of the NFSLMS algorithm, offers both fast convergence rate and low steady state error. Furthermore, by replacing the sigmoid function with the modified Versorial function, the modified CNFSLMS (MCNFSLMS) algorithm with low computational complexity is also presented. Experimental results illustrate that the combination scheme can behave as well as the best component and even better. Moreover, the MCNFSLMS algorithm requires less computational complexity than the CNFSLMS while keeping the same filtering performance.     

一种新的分割式比例仿射投影算法

针对自适应算法收敛速度和计算复杂度之间的矛盾．提出一种基于集员滤波的分割式比例仿射投影算法（SM-SPAPA）。该算法中只有当参数估计误差大于给定的误差门限时滤波器系数才进行迭代更新,从而能有效地减少滤波器系数的迭代次数。仿真结果表明,由于每次迭代将对误差性能贡献最大的输入信号筛选出来作为输入,从而能加快收敛速度,同时还能够减少算法的运算量。     

Robust extended multidelay filter and double-talk detector for acoustic echo cancellation

We propose an integrated acoustic echo cancellation solution based on a novel class of efficient and robust adaptive algorithms in the frequency domain, the extended multidelay filter (EMDF). The approach is tailored to very long adaptive filters and highly auto-correlated input signals as they arise in wideband full-duplex audio applications. The EMDF algorithm allows an attractive tradeoff between the well-known multidelay filter and the recursive least-squares algorithm. It exhibits fast convergence, superior tracking capabilities of the signal statistics, and very low delay. The low computational complexity of the conventional frequency-domain adaptive algorithms can be maintained thanks to efficient fast realizations. We also show how this approach can be combined efficiently with a suitable double-talk detector (DTD). We consider a corresponding extension of a recently proposed DTD based on a normalized cross-correlation vector whose performance was shown to be superior compared to other DTDs based on the cross-correlation coefficient. Since the resulting DTD also has an EMDF structure it is easy to implement, and the fast realization also carries over to the DTD scheme. Moreover, as the robustness issue during double talk is particularly crucial for fast-converging algorithms, we apply the concept of robust statistics into our extended frequency-domain approach. Due to the robust generalization of the cost function leading to a so-called M-estimator, the algorithms become inherently less sensitive to outliers, i.e., short bursts that may be caused by inevitable detection failures of a DTD. The proposed structure is also well suited for an efficient generalization to the multichannel case.     

适用于即时翻译系统的改进仿射投影算法 *

针对即时翻译系统应用中存在双端对讲干扰和模型噪声的问题,提出了一种适用于便携式即时翻译系统的改进变步长仿射投影算法。新算法在收敛步长中引入近端信号能量统计量和滤波器收敛程度统计量,根据统计量的改变实时调整步长参数,防止算法发散。仿真结果表明,与传统自适应滤波算法和改进仿射投影算法相比,所提出的算法不但可以有效克服双端对讲干扰,而且在收敛速度、稳态失调等方面也有明显改善。     

基于集员滤波的选择权更新子带有源噪声控制

通过子带自适应滤波结构,可以提高宽带噪声降噪效果,归一化子带自适应滤波（NSAF）通过在每个子带上使用相同的全带自适应滤波器,消除了传统子带结构会在输出端产生混叠分量的问题,具有较好的收敛性能和稳态均方误差。但由于在每个子带上采用相同的全带自适应滤波器,计算量要高于传统子带结构,集员滤波（SMF）技术具有数据选择更新的特点,可有效降低计算复杂度。基于NSAF结构,建立了前馈ANC无延迟结构,并基于集员滤波技术,通过选择部分权更新来进一步减少计算量,仿真验证了该算法对宽带噪声具有更优的降噪效果。