An efficient online secondary path estimation for feedback active noise control systems

In practical cases for active noise control (ANC), the secondary path has usually a time varying behavior. For these cases, an online secondary path modeling method that uses a white noise as a training signal is required to ensure convergence of the system. The modeling accuracy and the convergence rate are increased when a white noise with a larger variance is used. However, the larger variance increases the residual noise, which decreases performance of the system and additionally causes instability problem to feedback structures. A sudden change in the secondary path leads to divergence of the online secondary path modeling filter. To overcome these problems, this paper proposes a new approach for online secondary path modeling in feedback ANC systems. The proposed algorithm uses the advantages of white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the algorithm and to prevent the instability effect of the white noise. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to correct the secondary path estimation. In addition, the proposed method models the secondary path without the need of using off-line estimation of the secondary path. Considering the above features increases the convergence rate and modeling accuracy, which results in a high system performance. Computer simulation results shown in this paper indicate effectiveness of the proposed method.     

Nonlinear feedback active noise control for broadband chaotic noise

Feedback active noise control has been used for tonal noise only and it is impractical for broadband noise. In this paper, it has been proposed that the feedback ANC algorithm can be applied to a broadband noise if the noise characteristic is chaotic in nature. Chaotic noise is neither tonal nor random; it is broadband and nonlinearly predictable. It is generated from dynamic sources such as fans, airfoils, etc. Therefore, a nonlinear controller using a functional link artificial neural network is proposed in a feedback configuration to control chaotic noise. A series of synthetic chaotic noise is generated for performance evaluation of the algorithm. It is shown that the proposed nonlinear controller is capable to control the broadband chaotic noise using feedback ANC which uses only one microphone whereas the conventional filtered-X least mean square (FXLMS) algorithm is incapable for controlling this type of noise.     

A New Delayless Subband Adaptive Filtering Algorithm for Active Noise Control Systems

Subband adaptive filtering (SAF) techniques play a prominent role in designing active noise control (ANC) systems. They reduce the computational complexity of ANC algorithms, particularly, when the acoustic noise is a broadband signal and the system models have long impulse responses. In the commonly used uniform-discrete Fourier transform (DFT) -modulated (UDFTM) filter banks, increasing the number of subbands decreases the computational burden but can introduce excessive distortion, degrading performance of the ANC system. In this paper, we propose a new UDFTM-based adaptive subband filtering method that alleviates the degrading effects of the delay and side-lobe distortion introduced by the prototype filter on the system performance. The delay in filter bank is reduced by prototype filter design and the side-lobe distortion is compensated for by oversampling and appropriate stacking of subband weights. Experimental results show the improvement of performance and computational complexity of the proposed method in comparison to two commonly used subband and block adaptive filtering algorithms.      

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.     

An Improved Active Noise Control Algorithm Without Secondary Path Identification Based on the Frequency-Domain Subband Architecture

Common active noise control (ANC) algorithms need to identify the secondary path transfer functions between the output of the adaptive control filters and the error sensors, and then use the information to guide the direction of control filter coefficient updating. Recently, Zhou proposed an ANC algorithm without secondary path identification, and we improve their algorithm in this paper. For single-tone and narrowband noise control, the direction of control filter coefficient updating has four choices 180$^{circ}$, 0$^{circ}$, and $pm {hbox {90}}^circ$. We test the four update directions and select the one that works the best. If for all four update directions, the system converges slowly or diverges, we adjust the step size and test again with the new step size. The multitone and broadband noise control problems are converted into several single-tone and narrowband noise control problems by means of a frequency-domain delayless subband architecture. Compared to Zhou's algorithm, our proposed method yields good performance and converges quickly. Simulation results confirm the effectiveness of our proposed algorithm.      

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.     

A correlation test-based validity monitoring procedure for online detecting the quality of nonlinear adaptive noise cancellation

In the present study, a new correlation test-based nonlinear adaptive noise cancellation (ANC) validity monitoring procedure is proposed by following the insight and formulations which were developed by the authors for validating identified nonlinear dynamic models. The new method is based on the concept that if an ANC is valid, the recovered signal should be uncorrelated to the noise source. Then, a new correlation test between recovered signal and noise source is periodically computed to online check the validity of noise cancellers when ANCs are in operation. Simulation demonstrations on validity monitoring for recursive least squares-based ANC are conducted to illustrate the effectiveness and efficiency of the new procedure.     

基于小波变换和神经网络的车内噪声信号重构

为获取较高精度车内噪声主动控制(Active Noise Control, ANC)参考信号,提出了一种基于小波变换和BP神经网络的车内噪声信号重构方法。以在某轿车采集到的噪声信号为基础,用声学传递路径分析(TPA)方法确定影响车内噪声的关键点信号。鉴于噪声源信号对车内信号非线性关系的复杂性,建立BP神经网络的噪声重构模型,并利用小波分解来降低噪声信号的非平稳性。为对比重构效果,建立BP神经网络噪声重构模型。结果表明,本文提出算法的重构值与实测值之间的平均绝对误差比BP神经网络小,并且基于小波变换和BP网络重构模型的平均绝对误差均小于0.01。该方法能够对车内噪声信号进行准确、有效的重构。     

Optimal Variable Step-Size NLMS Algorithms With Auxiliary Noise Power Scheduling for Feedforward Active Noise Control

The paper introduces two improvements in the feedforward active noise control system with online secondary path modeling developed by Akhtar, Abe, and Kawamata: 1) optimal variable step-size parameters are derived for the adaptation algorithms of the secondary path modeling filter and of the control filter and 2) a self-tuning power scheduling for the auxiliary noise is introduced. The proposed power scheduling is chosen so that in every operating condition a specific ratio between the powers at the error microphone of the auxiliary noise and of the residual noise is achieved. It is shown that for the same auxiliary noise conditions the adaptation algorithms equipped with the optimal variable step-size parameters improve the convergence speed of the system and the estimation accuracy of the secondary path and of the optimal control filter. It is also shown that, compared with a fixed power auxiliary noise, the power scheduling of the auxiliary noise is capable to better meet the conflicting requirements of fast convergence speed of the secondary path modeling filter and of low residual noise in steady state conditions.      

Neural filtered-U algorithm for the application of active noise control system with correction terms momentum

This paper introduces a novel neural filtered-U recursive least mean square (NFURLMS) algorithm and its corresponding weight updating method to the application of active noise control (ANC) system. Instead of the complex designing procedures, the proposed approach uses few mathematical transfer functions to design the ANC system. The correction terms momentum to avoid the premature saturation of back-propagation algorithm and the way to design the optimal learning rate are also included in the paper to improve the noise reduction performance. In addition, the proposed method protects ANC systems against unstable poles such as occur in conventional filtered-U design. Several simulation results show that the proposed method can effectively cancel the narrowband and broadband noise in an ANC system.     

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.     

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.     

基于误差通道并行建模的主动控制系统

提出了基于误差通道并行在线建模算法的主动控制系统,该系统同时采用3个自适应滤波器,并且通过引入一个延迟单元,以保证滤波器解的唯一性。数学分析和仿真实验结果表明,该控制算法能获取误差通道的无偏估计,并可降低主动降噪系统总体代价。     

Active Narrowband Noise Control Systems Using Cascading Adaptive Filters

The disturbance picked up by error sensors can significantly degrade the steady-state performance of active noise control (ANC) systems for practical applications. This paper presents a cascading adaptive algorithm for removing uncorrelated disturbance and analyzes its performance in narrowband ANC systems. Theoretical analysis shows that the proposed algorithm improves the behavior of the adaptive filter in steady state. Computer simulations using the measured transfer functions validate its performance     

Time-shared channel identification for adaptive noise cancellation in breath sound extraction

Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. Traditional adaptive noise cancellation (ANC) methodologies work reasonably well when signal and noise are stationary and independent. Clinical lung sound auscultation encounters an acoustic environment in which breath sounds are not stationary and often correlate with noise. Consequently, capability of ANC becomes significantly compromised. This paper introduces a new methodology for extracting authentic lung sounds from noise-corrupted measurements. Unlike traditional noise cancellation methods that rely on either frequency band separation or signal/noise independence to achieve noise reduction, this methodology combines the traditional noise canceling methods with the unique feature of time-sprit stages in breathing sounds. By employing a multi-sensor system, the method first employs a high-pass fdter to elinfinate the off-band noise, and then performs time-shared bfind identification and noise cancellation with recursion from breathing cycle to cycle. Since no frequency separation or signal/noise independence is required, this method potentially has a robust and reliable capability of noise reduction, complementing the traditional methods.     

Adaptive signal processing algorithms for creating spatial zones of quiet

Available adaptive active noise control (ANC) algorithms can only minimize the noise level at a point that an error microphone is placed. Consequently, a zone of quiet around this microphone is produced as a byproduct. However, they cannot technically control or, even, monitor the noise level within the zone of quiet unless they use several sensors. They cannot also control the shape and the extension of the quiet zone by using only the error microphone. This paper develops a signal processing framework for the derivation of adaptive ANC algorithms that can directly create a controllable zone of quiet in monochromatic noise fields using a single error microphone. It is shown that by adding a filter to the standard ANC structure, a controllable zone of quiet is created. The transfer function of this filter is obtained using an accurate mathematical analysis. It is also shown that the extension of the zone of quiet can be controlled by tuning this filter. The implementation of the proposed system requires no additional hardware, rather than those required for traditional ANC systems. The validity of the results are discussed by using numerical analysis. Also, the performance of the proposed system is practically verified.     

利用麦克风阵列的管道主动噪声控制方法

空调通风管道在运行时, 内部出现的低频噪声很难通过包裹消音材料等被动式降噪方法消除. 而在部署主动噪声控制时, 会出现声反馈现象, 影响降噪性能甚至造成控制系统的响应发散. 针对这种声反馈现象, 本文在分析其产生原因的基础上, 将麦克风阵列作为前馈, 对Duvall-Frost结构的线性约束最小方差波束成形算法加入预调向,提出了利用麦克风阵列的管道主动噪声控制方法, 实现单方向拾取来自管道上游的噪声信号, 避免声反馈带来的影响. 并利用滤波器x最小均方误差(FxLMS) 算法作为自适应控制算法, 针对4种典型低频噪声, 在真实管道环境下进行主动降噪实验. 实验结果表明, 相比不使用麦克风阵列的情况, 本文提出的主动噪声控制方法能达到明显的降噪性能, 且在稳定性方面取得较好结果.     

An improved block adaptive system for effective feedback cancellation in hearing aids

The hearing aid being a battery operated, portable device requires short processing delay, low computational complexity, with appreciable acoustic feedback cancellation effect. The prediction error method (PEM) and PEM with shadow filter (PEM-SH) based adaptive feedback canceller (AFC) referred as PEMAFC and PEMAFC-SH respectively reduces the amount of bias present in the estimate of feedback path. The available partitioned block frequency domain adaptive filter (PBFAF) based implementation of PEMAFC (PBFAF-P) and PEMAFC-SH (PBFAF-PS), offers a potential option for modelling an adaptive filter with many taps along with short block processing delay. However, the PBFAF suffers from large computational load because of the involvement of computationally expensive gradient constraints in each partition. Though removing or alternately applying the gradient constraint saves some computations but it results in significant performance degradation. With an objective of substantially reducing the computational burden and simultaneously retaining the performance, this paper develops an improved partitioned block Hartley domain adaptive filter (IPBHAF) and then employs it for effective feedback cancellation in hearing aids. Further, the IPBHAF with modified step size (IPBHAF-M) is proposed to achieve both fast convergence and better steady state performance. The simulation based experiments demonstrate the superior performance of IPBHAF-M based implementations of PEMAFC (IPBHAF-MP) and PEMAFC-SH (IPBHAF-MPS) over the PBFAF-P and PBFAF-PS in terms of both computational complexity and feedback cancellation performance.     

非高斯噪声下的回声消除算法研究

回声消除一直是信号处理领域的热门研究方向,其中自适应滤波器是在回声消除问题中最为广泛应用的技术,但自适应滤波算法主要是在基于高斯噪声条件下的应用,而现实环境广泛存在着非高斯的噪声,这严重影响了基于L2范数的自适应噪声滤波算法的噪声消除性能。为解决回声消除方法对非高斯噪声的适用性问题,根据回声路径具有明显的稀疏系统特性,结合比例矩阵的设计思想以及符号算法(SA),提出一种改进的MIPNSA算法。该滤波算法既能很好地适应于不同的背景噪声,同时也在较大程度上增强了对稀疏系统的适应能力。仿真测试结果表明,在高斯噪声和非高斯噪声条件下,本算法比现有的一些算法的回声消除效果更佳。     

数字助听器系统中的回声消除方法

提出一种数字助听器回声消除方法,该方法通过引进预滤波单元,解决了由于前向路径存在而导致数字助听器系统受话器输出信号与麦克风输入信号存在相关性的问题,进而保证了对回声路径的自适应估计算法收敛于无偏估计。提出了数字助听器回声路径估计的自适应次梯度投影算法,相比传统的NLMS自适应算法收敛速度更快,收敛精度更高,对噪声鲁棒性强。使用白噪声和真实语音信号的仿真实验证明：对于长的房间回声路径或短的助听器回声路径,该算法都能快速准确地收敛到正确路径。