基于小波神经网络的次级通路建模研究

在有源噪声控制系统实现的过程中,次级通路建模精度对有源控制算法实现、系统的稳定性及降噪量都有重要影响。提出了一种基于小波神经网络的次级通路建模方法,研究了其结构化设计方法和相应的学习算法,并通过计算机仿真验证了其有效性。     

利用参量阵扬声器进行有源噪声控制的研究

在有源噪声控制系统中,由于传统扬声器具有全指向性,在控制点处获得降噪效果的同时,在声场其他区域噪声会增大.参量阵扬声器是具有强指向性的新型声频声源,可以作为有源噪声控制系统中的次级声源,以实现特定方向或区域的降噪,同时减少对声场其他区域的影响.对参量阵扬声器和传统扬声器作为次级声源的有源噪声控制系统进行了仿真实验,结果...     

基于神经网络的风机管道噪声有源控制的实验研究

通过实验来考察空气流动对传声器测量信号的影响，给出了基于神经网络的风机管道噪声有源控制的实验结果。实验结果表明，风机管道中空气流动对传声器测量信号的影响很大。在实验中采用了几项保护措施，使得次级路径的辨识结果对风机管道中空气流动不再敏感。实验结果表明了基于神经网络的风机管道噪声有源控制系统的有效性。     

建筑施工有源振动噪声主动抑制系统设计

针对绿色建筑施工过程中的有源噪声问题,设计一种新的建筑施工有源振动噪声主动抑制系统,抑制建筑施工产生的高分贝噪声,避免影响当地居民生活、损害施工场地管理人员的听力。该系统通过传声器MIC1和MIC2采集建筑施工有源振动噪声信号后,采用LMS自适应滤波算法,去除噪声信号中干扰因子并通过数控放大器放大,将放大后的噪声信号传输至信号处理器中,先采用基于梯度下降的次级通道在线建模有源噪声控制方案,准确计算次级通道传递函数,再设定抑制噪声指令,叠加噪声控制信号与噪声信号,实现有源振动抑噪。经验证,某建筑施工场地降噪前噪声最大分贝高达79 dB,使用该系统后,噪声分贝数值降低到27～32 dB之间。实验不仅验证了该系统的有效性,还验证了系统具有较高的降噪量与降噪速度优势。     

利用位置估计进行船舶噪声低频线谱有源控制

提出了一种基于接收点位置估计的船舶航行噪声低频线谱有源控制方法。利用船壳上布设的次级声源实现低频线谱的有源抵消,其信号处理使用了估计的接收点位置信息。通过仿真分析了位置估计误差对船舶航行过程中低频线谱信号有源降噪效果的影响。仿真结果表明,方位估计误差可控时该方法降噪效果良好。     

封闭空间内的多模型自适应有源前馈噪声控制

误差通道建模是实现自适应有源噪声控制算法的重要环节.由于次级通路建模误差对整个系统稳定性有重要影响,利用Modified FXLMS算法结合多模型自适应控制与封闭空间误差通道特性,提出一种针对封闭空间自适应有源控制的多模型算法,完成了该算法的性能分析和计算机仿真.     

基于DSP的耳机噪声抵消系统的设计与实现

设计和实现了基于DSP的自适应有源降噪耳机系统。分析了有源降噪耳机系统的原理,基于SEED-DEC6416开发板实现了有源降噪耳机系统的硬件和软件设计,为保证系统的实时性对程序进行优化,降噪耳机系统实现了对实际环境中噪声信号的提取、自适应滤波和噪声抵消。实验结果表明系统,在实际噪声环境中可以对噪声进行抵消,并良好地恢复语音信号,验证了系统设计的可行性和算法的正确性。     

一种在线建模信号分段调控的有源噪声控制算法

在前馈有源噪声控制系统中,在线建模模块需附加建模信号,当控制系统接近收敛时,建模信号的存在会导致系统降噪性能变差.针对此问题,提出一种在线建模信号按能量进行分段调控的算法,改进了建模滤波器的更新方式.针对变压器噪声源,不同算法的仿真对比分析结果表明,改进算法对宽带低频信号有更好的控制效果,不仅提升了系统降噪量,也加快了...     

有源控制中通路建模模型研究

在有源控制通路建模或有源控制实现过程中，与FIR滤波器相比，虽然IIR滤波器具有诸多优点，但由于其对系统稳定性的不利影响及其工程实现的不易性，在实际通路建模时，更多地是使用FIR滤波器模型。重点研究使用FIR滤波器对包含零极点的通路传递函数进行建模时，传递函数极点位置变化对滤波器长度的影响以及极点个数对滤波器长度的影响。     

头戴式有源噪声控制系统的分区域优化控制方法研究

本文提出了一种适用于头戴式有源噪声控制系统的有源降噪方法,结合虚拟传声器技术,并通过将水平面划分为多个子区域,实现人耳处对不同方向噪声的有效控制。初级声场传递函数的实际值与其估计值的偏差是影响头戴式有源噪声控制系统降噪量的主要因素,因此需要优化一定角度范围内的初级声场传递函数,本文提出一种基于最大化该区域最小降噪量的优化设计方法。最后,将水平面划分为不同子区域,每个子区域分别使用对应的优化初级声场传递函数进行噪声控制。实验结果验证了该噪声控制方法的有效性。      

Adaptive Volterra filters for active control of nonlinear noiseprocesses

This paper presents a Volterra filtered-X least mean square (LMS) algorithm for feedforward active noise control. The research has demonstrated that linear active noise control (ANC) systems can be successfully applied to reduce the broadband noise and narrowband noise, specifically, such linear ANC systems are very efficient in reduction of low-frequency noise. However, in some situations, the noise that comes from a dynamic system may he a nonlinear and deterministic noise process rather than a stochastic, white, or tonal noise process, and the primary noise at the canceling point may exhibit nonlinear distortion. Furthermore, the secondary path estimate in the ANC system, which denotes the transfer function between the secondary source (secondary speaker) and the error microphone, may have nonminimum phase, and hence, the causality constraint is violated. If such situations exist, the linear ANC system will suffer performance degradation. An implementation of a Volterra filtered-X LMS (VFXLMS) algorithm based on a multichannel structure is described for feedforward active noise control. Numerical simulation results show that the developed algorithm achieves performance improvement over the standard filtered-X LMS algorithm for the following two situations: (1) the reference noise is a nonlinear noise process, and at the same time, the secondary path estimate is of nonminimum phase; (2) the primary path exhibits the nonlinear behavior. In addition, the developed VFXLMS algorithm can also be employed as an alternative in the case where the standard filtered-X LMS algorithm does not perform well     

Active noise control using an adaptive bacterial foraging optimization algorithm

This paper presents an adaptive bacterial foraging optimization (ABFO) algorithm for an active noise control system. The conventional active noise control (ANC) systems often use the gradient-based filtered-X least mean square algorithms to adapt the coefficients of the adaptive controller. Hence, there is a possibility to converge to local minima. In addition, this class of algorithms needs prior identification of the secondary path. The ABFO algorithm helps the ANC system to prevent falling into local minima. The proposed ANC system is also simpler since it does not need any prior information of the secondary path. Moreover, the adaptive strategy of the algorithm results in improved search performance compared with the basic bacterial foraging optimization algorithm, as well as other conventional algorithms. Experimental studies are performed for nonlinear primary path along with linear and nonlinear secondary path. The results show the effectiveness of the proposed ABFO-based ANC system for different kinds of input noise.     

Online secondary path modeling in multichannel active noise control systems using variable step size

In single-channel feedforward active noise control (ANC) systems, additive random noise based methods are often applied to achieve secondary path modeling (SPM) during online operation. This paper investigates the issue of online SPM in multichannel ANC systems. It is shown that the application of existing methods for online SPM in multichannel ANC systems greatly increases the computational complexity. Here we extend our previous work on single-channel variable step-size online SPM to multichannel ANC systems. It is shown that the proposed method has reduced computational complexity as compared with other methods. Computer simulations are carried out that demonstrate the effectiveness of the proposed method.     

基于虚拟传声的双通道有源噪声控制

以有源耳罩为对象研究了基于虚拟传声的双通道有源噪声控制问题。双通道即在传统单通道有源噪声控制系统的基础上增加一个次级通路。由于两个通道之间存在相互耦合,因此控制难度加大。使用基于虚拟传声器技术的自适应多通道FxLMS算法,应用一阶前向差分预测算法通过自适应变权值预测控制目标区域内的残余误差信号。仿真结果表明,通过该方法可以使目标静音区的噪声信号明显衰减。     

噪声有源自校正预测控制

有源噪声控制（ANC）的经典方法是采用有限脉冲响应（FIR）滤波器的Filter-X算法，该算法的一个主要缺点是次级声路径响应对控制滤波器参数自适应的收敛速度有较大影响。将预测控制方法应用到有源噪声控制领域，给出了一种参数在线自适应算法，该算法的收敛速度不受次级声路径响应的影响。仿真结果表明，给出的控制方法比传的Filter-X控制有更小的稳态误差，而且收敛速度更快。     

基于鲁棒控制的管道噪声有源控制实验研究

以鲁棒控制理论为基础,详细研究了基于H∞鲁棒控制理论的有源控制器设计.同时,为了验证该鲁棒控制器的有效性,在消声室内以管道噪声为研究对象,进行了一系列相关实验验证.实验结果充分表明了该鲁棒控制器的控制性能.     

Feedback active noise control based on forward–backward LMS predictor

In this paper, a new feedback active noise control (FBANC) system based on forward–backward error LMS (FBLMS) predictor is proposed. The misadjustment of the FBLMS predictor is about half that of the forward error LMS (FLMS) predictor. The new ANC system employs FBLMS predictors both for its main path (MP) predictor and for the noise canceler (NC) for the secondary path (SP) identification (SPI). To realize the MP predictor based on the FBLMS concept, a new FXLMS structure is proposed. But for the NC for the SPI, the FBLMS predictor is directly used. The MP predictor based on FBLMS reduces its misadjustment. Further the use of FBLMS predictor for the NC, as it gives a good prediction of primary noise component in the error (residual noise), improves the SNR for SPI. Thus, the improved SP estimate and the reduced misadjustment for the MP predictor achieved result in a significantly better overall noise reduction (of about 8 dB) over the ANC that uses the MP predictor and noise canceler for SPI, both based only on the forward error LMS algorithm. The computational load for the proposed algorithm is about twice that of FBANC that uses only forward error.     

噪声有源控制的人工神经网络方法

讨论了有源噪声控制（ＡＮＣ）问题,提出一种基于人工神经网络的非线性噪声有源自适应控制方法,给出了一种基于误差梯度下降的学习算法,证明了闭环控制系统在Ｌｙａｐｕｎｏｖ意义下的稳定性。     

Variable step-size Griffiths’ algorithm for improved performance of feedforward/feedback active noise control

A new robust computationally efficient variable step-size LMS algorithm is proposed and it is applied for secondary path (SP) identification of feedforward and feedback active noise control (ANC) systems. The proposed variable step-size Griffiths’ LMS (VGLMS) algorithm not only uses a step-size, but also the gradient itself, based on the cross-correlation between input and the desired signal. This makes the algorithm robust to both stationary and non-stationary observation noise and the additional computational load involved for this is marginal. Further, in terms of convergence speed and error, it is better than those by the Normalized LMS (NLMS) and the Zhang’s method (Zhang in EURASIP J. Adv. Signal Process. 2008(529480):1–9, 2008). The convergence rate of the feedforward and feedback ANC systems with the VGLMS algorithm for SP identification is faster (by a factor of 2 and 3, respectively) compared with that using NLMS algorithm. For feedforward ANC, its convergence rate is faster (3 times) compared with Akhtar’s algorithm (Akhtar in IEEE Trans Audio Speech Lang Process 14(2), 2006). Also, for higher main path lengths compared with SP, the proposed algorithm is computationally efficient compared with Akhtar’s algorithm.     

New unbiased adaptive IIR filter: it’s robust and variable step-size versions and application to active noise control

This paper proposes a robust variable step-size adaptive IIR filter realized by a new bias-free structure (BFS). Unlike equation error (EQE) method that uses a desired signal contaminated with observation noise, the BFS employs a filter driven by the output of the plant estimate and this achieves a bias-free estimate of the denominator of the system function. In addition, the adaptation is made robust to the observation noise by the Griffiths’ LMS adaptation, which uses the cross-correlation estimate between the input and the desired signal for its adaptation gradient computation. A robust variable step-size adaptation is also realized by the Griffiths’ gradient. The proposed structure is referred to as BFSGV and has good modeling capability with improved convergence rate and reduced misadjustment. For system identification, the proposed BFSGV algorithm gives a 3 dB improvement in the performance index over EQE method. The proposed BFSGV has been applied to active noise control (ANC). The BSFGV structure is used for secondary path (SP) estimation, and for the main path (MP), BFS structure with step-size varied according to Okello’s method (BSFV) is used. The new ANC system for narrowband noise field is found to be having 4 times faster convergence rate and an additional noise reduction of 15dB over that FIR for MP and the EQE for SP. Further, the use of the proposed ANC IIR algorithm achieves computational savings compared to that of FIR. For the broadband noise field, the proposed method that uses BSFV for MP and BSFGV for SP provides 18 times faster convergence rate and 2.5 dB reduction in ANC error compare to that of the ANC using FIR for MP and the EQE for SP estimation.