基于sigmoid函数的Volterra自适应有源噪声对消器

该文介绍了一种新颖的非线性自适应有源噪声对消器基于sigmoid函数的Volterra自适应有源噪声对消器,并采用输入信号和瞬时误差归一化的LMS自适应算法调整其系数。这种基于sigmoid函数的Volterra自适应有源噪声对消器具有参数少和便于实现的模快化结构等优点。仿真结果表明:这种基于sigmoid函数的Volterra自适应有源噪声对消系统具有良好的抗噪声性能。     

一种基于格型正交化的二阶Volterra自适应滤波算法

针对 Volterra 自适应滤波器输入信号相关性或附加的非线性畸变的增强使自适应滤波器性能下降的问题,本文提出基于格型正交化的二阶 Volterra 自适应滤波算法.先对输入信号进行格型预处理,得到互相正交的后向预测误差信号;然后将其作为自适应滤波器的输入,从而大大降低了一次项、平方项和交叉乘积项信号各项之间的耦合,改善了自适应算法的收敛性能.有源噪声对消的仿真结果表明,在输入噪声强相关和附加较强非线性畸变时本算法仍具有较好的消噪性能.     

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。     

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。     

基于听觉场景分析的电子听诊器降噪算法研究

针对在医院和救护车等噪声环境下使用的电子听诊器,提出了一种与噪声的类型无关的、基于人耳听觉场景分析的心肺音降噪CASA算法。该算法利用双通道采集到的信号和噪声的能量的不同,模仿人耳提取双通道之间强度差(IID)特征,以此为依据来辨别信号和噪声。再利用声学掩蔽效应,对噪声进行掩蔽,实现目标心肺音的增强。与自适应NLMS滤波相比,CASA的效果要好16 dB以上。     

噪声对消在信号处理系统中的应用

论述基于LMS算法的自适应滤波器噪声对消的工作原理,以及基于AR模型的信号分析方法。在这两种方法相结合的情况下,能有效去除信号的噪声。对含有瞬态干扰的微弱信号,用AR模型法估计出噪声的系数并预测噪声波形,通过自适应滤波器噪声对消原理进行滤波,最后在Matlab环境下进行仿真试验,结果表明该方法具有较好的去噪效果。     

带有噪声相关性的自适应噪声抵消系统仿真研究

本文为了在语音信号处理中能消除含噪语音信号中的背景噪音,采用自适应信号处理的理论和技术来达到提高语音信号质量的目的。通过介绍自适应滤波器原理,在对自适应滤波器相关理论研究的基础上,研究了LMS自适应滤波算法,并对LMS自适应算法进行了分析。同时为了使输入的参考信号与噪声相关,加入分离周期信号与带有窄带干扰抑制的宽带信号。通过分析仿真结果表明基于LMS算法的自适应噪声抵消技术可以有效地抵消正弦干扰信号,同时加入宽带信号中的周期性噪声,在没有另外的与噪声相关的参考信号的情况下,可以使用自适应噪声抵消系统来消除这种同期性干扰噪声。     

基于DSP的自适应有源噪声控制系统

文章介绍了基于TITMS32 0F2 4 0DSP的自适应有源噪声控制系统 ,给出了系统的硬件结构及其工作原理 ,详细说明了有源噪声控制的自适应算法 ,并给出了程序流程图及实验结果。通过实验证明 ,该系统有较好的降噪效果。     

基于TMS320VC5509 DSP的有源噪声控制系统设计与实现

介绍了有源噪声控制技术的理论基础和算法,以自适应有源噪声前馈控制系统为研究核心,选用TMS320VC5509 DSP作为控制器,给出了系统的硬件解决方案,并用C语言编程在硬件系统上实现了基于FX-LMS算法的有源噪声实时控制。对800Hz单频噪声的实验结果表明系统可降低噪声幅度9dB。     

一种非线性变步长LMS自适应滤波算法

基于定步长LMS自适应滤波算法存在稳态误差和收敛速度无法合理协调的缺点,通过在误差信号与步长之间建立非线性关系,提出了一种新的变步长LMS算法。通过步长函数可以让步长因子在算法初始阶段具有较大值,在算法收敛阶段获得较小值,且变化平缓。理论分析和实验仿真结果表明,新算法克服了在稳态自适应阶段步长调整导致系统不稳定的问题。将新算法应用于电脑风扇噪声的有源噪声控制中,取得了良好的效果。     

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.     

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     

基于延迟LMS算法的管道噪声有源控制实验研究

在自适应有源控制中，次级通路建模对控制器算法实现有着重要的影响。以往次级通路建模通常会使用带有数十个甚至上百个系数的FIR滤波器，但过多的参数运算会对系统稳定性、控制实时性及硬件复杂度等方面带来麻烦。研究了一种在工程中实现简便的次级通路建模方法——通路时延估计法，并在消声室内进行了管道噪声有源控制实验。实验结果表明，次级通路时延影响着管道噪声有源控制的降噪频率以及降噪带宽。     

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。     

Saturation effects in active noise control systems

The reference and error sensors of active noise control (ANC) systems will be saturated in real-world applications if the noise level exceeds the dynamic range of the sensors. However, there is a lack of analysis of saturation effects on the performance of ANC systems. This paper proposes an indirect method for analyzing the saturation effects in steady state using Fourier analysis. This indirect method uses clipping to approximate saturation and decomposes the saturated narrowband signal as the summation of a set of rectangular waves and a pulse-amplitude modulated signal. The theoretical analysis shows that the clipping of a sinusoidal signal produces extra odd harmonics, thus affecting the convergence speed and steady-state solution of adaptive filter in ANC systems. This analysis can be extended to narrowband noises that consist of multiple sinusoidal components such as engine noise in many ANC applications. A low-pass filter is effective in reducing saturation effects for harmonic-related noises. Analysis results are verified by computer simulations using recorded engine noise and transfer functions measured from an experimental setup.     

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.     

基于虚拟传感器有源噪声控制系统及实时实现

详细推导了基于虚拟传感器的有源噪声控制系统的算法结构。在TMS320C6727硬件平台上,采用LMS算法完成了误差通道模型的传函辨识,用FXLMS算法对虚拟传感器的系统性能进行了试验分析。试验结果表明,该方法可以有效地将噪声控制范围向虚拟传感器处转移。     

An efficient feedback active noise control algorithm based on reduced-order linear predictive modeling of FMRI acoustic noise

Functional magnetic resonance imaging (fMRI) acoustic noise exhibits an almost periodic nature (quasi-periodicity) due to the repetitive nature of currents in the gradient coils. Small changes occur in the waveform in consecutive periods due to the background noise and slow drifts in the electroacoustic transfer functions that map the gradient coil waveforms to the measured acoustic waveforms. The period depends on the number of slices per second, when echo planar imaging (EPI) sequencing is used. Linear predictability of fMRI acoustic noise has a direct effect on the performance of active noise control (ANC) systems targeted to cancel the acoustic noise. It is shown that by incorporating some samples from the previous period, very high linear prediction accuracy can be reached with a very low order predictor. This has direct implications on feedback ANC systems since their performance is governed by the predictability of the acoustic noise to be cancelled. The low complexity linear prediction of fMRI acoustic noise developed in this paper is used to derive an effective and low-cost feedback ANC system.     

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.     

Nonlinear adaptive bilinear filters for active noise control systems

The reference and error channels of active noise control (ANC) systems may be saturated in real-world applications if the noise level exceeds the dynamic range of the electronic devices. This nonlinear saturation degrades the performance of ANC systems that use linear adaptive filters with the filtered-X least-mean-square (FXLMS) algorithm. This paper derives a bilinear FXLMS algorithm for nonlinear adaptive filters to solve the problems of signal saturation and other nonlinear distortions that occur in ANC systems used for practical applications. The performance of this bilinear adaptive filter is evaluated in terms of convergence speed, residual noise in steady state, and the computational complexity for different filter lengths. Computer simulations verify that the nonlinear adaptive filter with the associated bilinear FXLMS algorithm is more effective in reducing saturation effects in ANC systems than a linear filter and a nonlinear Volterra filter with the FXLMS algorithm.