基于谱减和LMS的自适应语音增强

针对宽带噪声背景下的语音增强问题,将短时语音视为非平稳或宽平稳信号,基于谱减法和自适应滤波的最小均方（LMS）算法,提出了一种FIR型自适应滤波算法（SSLMS）：用减谱法由短时噪声观测语音估计期望信号,作为滤波器输出信号的参考信号;用滤波器的输出与参考信号的差值为误差信号,用LMS算法求得滤波器权系数修正量,并修正滤波器。权系数最速下降调整中,采用了归一化LMS、符号LMS、块LMS技术,以简化保证权系数收敛的步长选择、减少权系数修正的运算量,从而提高自适应速度。对不同的语音在各种信噪比下仿真实验,并与改进的谱减法比较,结果表明,该法增强效果优于谱减法;在信噪比为3 dB时该法的增强效果仍然令人满意。     

基于盲信号分离的回波抵消技术

针对数字直放站回波抵消技术中自适应滤波法在多径回波信道条件下不能完全消除次径回波的问题,提出了基于盲信号分离的直放站回波抵消方法。首先对施主天线接收的混合信号进行相空间重构,使观测信号的数目大于等于独立信源的数目;然后利用独立分量分析法（ICA）对重构的信号进行盲信号分离;最后根据各分离信号和发送信号的相关情况判断有用信号,实现回波消除。对复杂多径回波信道条件下的多载波全球移动通信系统（GSM）信源进行回波抵消测试,分离得到的有用信号的相关系数可以达到0.9593。表明盲信号分离的方法可以实现复杂多径信道下的直放站回波抵消,有效解决了传统的自适应滤波法存在的问题。     

基于改进BP-LMS自适应滤波器算法的仿真研究

传统的LMS自适应滤波器收敛速度较慢,存在步长、收敛速度和失调的矛盾.针对此问题,提出了一种基于神经网络控制的自适应滤波器变步长算法,其中神经网络结构选择改进后BP神经网络模型,在反向传播过程中加入动量因子和自适应学习方法.使用自适应滤波器输入信号、输出信号、误差和步长因子建立网络关系(BP-LMS),确定学习步骤进行...     

一种快速收敛的变步长自适应谐波检测算法

为解决传统固定步长LMS自适应算法在电网谐波检测中存在的收敛速度和稳态误差之间的矛盾,本文提出了一种快速收敛的变步长自适应谐波检测算法。该算法以误差反馈信号、误差信号在总误差信号中所占的比率以及负载电流的相邻两个采样值之差的和作为自适应反馈量,并通过自适应反馈量的相干平均估计来控制步长的更新;同时对系统权值迭代公式进行改进提高收敛速度;并改传统的固定步长变化范围为时变范围,使步长变化更加平滑。该方法在负载突变的情况下有很好的跟踪性能,可有效的提高初始收敛速度、减小稳态失调。仿真分析及实验证明了该算法在谐波检测中的有效性和准确性。     

一种具有噪声干扰的MIMO非线性时变系统自适应控制

针对含噪声多输入多输出不确定非线性时变系统,提出一种基于多维泰勒网(MTN)的自适应控制方案,其中两个MTN分别用来实现优化控制和非线性滤波.首先,提出多维泰勒网控制器(MTNC)以实现实时跟踪控制.将滤波输出与期望值之间的闭环误差作为MTNC的输入,根据系统不确定因素引起的误差,基于稳定的学习率,设计线性再励的自适应变步长算法以快速更新MTNC权值.其次,提出多维泰勒网滤波器(MTNF)以消除测量噪声.由于定义了测量值与MTNF输出之间误差的Lyapunov函数,自适应MTN滤波系统兼具基于Lyapunov理论的自适应滤波(LAF)和MTN的特有性质.最后,通过在Lyapunov意义下选取适当的权值更新律,可使MTNF输出渐近地收敛到期望信号,并证明了滤波器的收敛性和稳定性.仿真结果验证了所提出方案的有效性.     

α稳定分布下Volterra滤波器的自适应数据块算法

基于分数低阶统计量原理提出了α稳定分布下Volterra滤波器的数据块滤波算法。该算法对Volterra滤波器权向量的线性项部分和非线性项部分分别采用不同的收敛因子,克服了传统只采用一个收敛因子的Volterra滤波器算法收敛性能差缺点,利用更多的输入信号和误差信号信息,更好地估计梯度,更精确地调节自适应滤波器权向量,提高了收敛速度。仿真结果验证了该方法的优越性。     

基于自适应滤波算法的伪码捕获研究与改进

针对直接扩频伪随机噪声码序列的捕获,本文提出一种改进的自适应滤波结构.该结构采用2个不同算法的并行滤波器,根据最小均方误差准则动态调整滤波器权值,达到了加快伪码捕获系统收敛速度、提高稳态性能的效果.分析和仿真证明该方案捕获时间短、结构简单有利于硬件实现.     

一种改进的LMS滤波器在DCG去噪中的应用

研究了LMS自适应滤波器在动态心电信号去噪中的应用。提出了一种适合动态心电信号预处理的变步长LMS改进算法,该算法用误差信号对期望信号的相对误差的平方根来调节步长。实验表明,这种改进滤波器在收敛速度和信噪比两方面都优于固定步长的滤波器。     

一种改进的集员滤波仿射投影算法

在优化算法的研究中,集员滤波的仿射投影算法具有比传统仿射投影算法迭代次数少、计算量小等优点,但实际应用中,算法的计算复杂度与滤波器长度成正比,当滤波器长度较大时限制了实时实现.为减少误差,实时运行,提出了一种改进算法.算法中当输出误差小于给定误差门限时,滤波器系数不必进行调节;否则将滤波器系数分成多个系数子集,通过仅更新权系数某一子集的方法,减少了原算法每次迭代中需更新的抽头权系数个数.采用一个声回波消除方法进行仿真.仿真验证了改进算法的性能,表明新算法与基于集员滤波的仿射投影算法相比,具有更快的收敛速度,并降低了计算复杂度.     

一种改进的变步长LMS自适应滤波算法及其在噪声抵消中的应用

本文提出了一种改进的变步长LMS自适应滤波算法,并将其应用于自适应噪声抵消中。该算法解决了算法收敛时间和稳态误差间的矛盾,为实际应用提供了更大的灵活性。它采用误差信号的相关值去调节步长,使得算法的均方误差小、收敛速度快,并且降低了LMS算法对噪声的敏感性。     

Adaptive Combination of Proportionate Filters for Sparse Echo Cancellation

Proportionate adaptive filters, such as those based on the improved proportionate normalized least-mean-square (IPNLMS) algorithm, have been proposed for echo cancellation as an interesting alternative to the normalized least-mean-square (NLMS) filter. Proportionate schemes offer improved performance when the echo path is sparse, but are still subject to some compromises regarding their convergence properties and steady-state error. In this paper, we study how combination schemes, where the outputs of two independent adaptive filters are adaptively mixed together, can be used to increase IPNLMS robustness to channels with different degrees of sparsity, as well as to alleviate the rate of convergence versus steady-state misadjustment tradeoff imposed by the selection of the step size. We also introduce a new block-based combination scheme which is specifically designed to further exploit the characteristics of the IPNLMS filter. The advantages of these combined filters are justified theoretically and illustrated in several echo cancellation scenarios.      

An Improvement of the Two-Path Algorithm Transfer Logic for Acoustic Echo Cancellation

Adaptive filters for echo cancellation generally need update control schemes to avoid divergence in case of significant disturbances. The two-path algorithm avoids the problem of unnecessary halting of the adaptive filter when the control scheme gives an erroneous output. Versions of this algorithm have previously been presented for echo cancellation. This paper presents a transfer logic which improves the convergence speed of the two-path algorithm for acoustic echo cancellation, while retaining the robustness. Results from simulations show an improved performance, and a fixed-point DSP implementation verifies the performance in real-time     

An improved new error estimation algorithm for optimal filter lengths for stereophonic acoustic echo cancellation

In this paper, we propose an new error estimate algorithm (NEEA) for stereophonic acoustic echo cancellation (SAEC) that is based on the error estimation algorithm (EEA) in [Nguyen-Ky T, Leis J, Xiang W. An improved error estimate algorithm for stereophonic acoustic echo cancellation system. In: International conference on signal processing and communication systems, ICSPCS’2007, Australia; December 2007]. In the EEA and NEEA, with the minimum error signal fixed, we compute the filter lengths so that the error signal may approximate the minimum error signal. When the echo paths change, the adaptive filter automatically adjusts the filter lengths to the optimum values. We also investigate the difference between the adaptive filter lengths. In contrast with the conclusions in [Khong AWH, Naylor PA. Stereophonic acoustic echo cancellation employing selective-tap adaptive algorithms. IEEE Trans Audio, Speech, Lang Process 2006;14(3):785-96, Gansler T, Benesty J. Stereophonic acoustic echo cancellation and two channel adaptive filtering: an overview. Int J Adapt Control Signal Process 2000;4:565-86, Benesty J, Gansler T. A multichannel acoustic echo canceler double-talk detector based on a normalized cross-correlation matrix. Acoust Echo Noise Control 2002;13(2):95-101, Gansler T, Benesty J. A frequency-domain double-talk detector based on a normalized cross-correlation vector. Signal Process 2001;81:1783-7, Eneroth P, Gay SL, Gansler T, Benesty J. A real-time implementation of a stereophonic acoustic echo canceler. IEEE Trans. Speech Audio Process 2001;9(5):513-23, Gansler T, Benesty J. New insights into the stereophonic acoustic echo cancellation problem and an adaptive nonlinearity solution. IEEE Trans. Speech Audio Process 2002; 10(5):257-67, Benesty J, Gansler T, Morgan DR, Sondhi MM, Gay SL. Advances in network and acoustic echo cancellation. Berlin: Springer-Verlag; 2001], our simulation results have shown that the filter lengths can be different. Our simulation results also confirm that the NEEA is better than EEA and SM-NLMS algorithm in terms of echo return loss enhancement.     

Multichannel Bin-Wise Robust Frequency-Domain Adaptive Filtering and Its Application to Adaptive Beamforming

Least-squares error (LSE) or mean-squared error (MSE) optimization criteria lead to adaptive filters that are highly sensitive to impulsive noise. The sensitivity to noise bursts increases with the convergence speed of the adaptation algorithm and limits the performance of signal processing algorithms, especially when fast convergence is required, as for example, in adaptive beamforming for speech and audio signal acquisition or acoustic echo cancellation. In these applications, noise bursts are frequently due to undetected double-talk. In this paper, we present impulsive noise robust multichannel frequency-domain adaptive filters (MC-FDAFs) based on outlier-robust M-estimation using a Newton algorithm and a discrete Newton algorithm, which are especially designed for frequency bin-wise adaptation control. Bin-wise adaptation and control in the frequency-domain enables the application of the outlier-robust MC-FDAFs to a generalized sidelobe canceler (GSC) using an adaptive blocking matrix for speech and audio signal acquisition. It is shown that the improved robustness leads to faster convergence and to higher interference suppression relative to nonrobust adaptation algorithms, especially during periods of strong interference     

改进的NLMS算法在回声消除系统中的应用

回声消除系统使用自适应滤波器模拟回声信道,自适应滤波器的阶数对回声消除效果具有重要的影响,合适的阶数可以提高回声消除效果。根据滤波器活跃区域权值可以模拟出基本的回声信道的原理,改进的NLMS算法首先将滤波器的初始权值阶数分帧,并求取每帧内权值的均值与抖动状态,然后通过比较阈值结果调整滤波器权值的活跃区域,摒弃非活跃权值区域,最终根据活跃权值的总帧数调整滤波器的阶数。改进的NLMS算法不仅保留了NLMS算法的结构简单、快速收敛的优点,而且实时调整滤波器阶数,降低稳态误差。通过回声消除仿真对比实验显示,算法的滤波器失准系数低于NLMS、PNLMS算法。     

基于附加信号的回波抵消技术

针对移动通信网络中数字直放站的空间同频干扰问题,提出一种基于附加信号的回波抵消算法。首先对直放站真实回波信号的成因及特性进行了研究,建立了真实回波信道参数矩阵的模型; 然后在直放站接收的基站下行载波信号频谱空穴处附加单频正弦信号,利用直放站接收的混合信号、转发信号与附加信号之间互相关函数的卷积关系估计出回波信道的参数矩阵; 最后将接收的混合信号与转发信号通过估计回波信道得到的模拟回波相减,达到消除回波干扰的目的。仿真结果表明,该算法在COST 207标准的频率选择性衰落信道模型下,主径衰减系数的相对误差为1.8493×10-5,模拟回波能较好地跟踪真实回波,从而有效避免自激现象。     

A hierarchical alternative updated adaptive Volterra filter with pipelined architecture

The pipelined adaptive Volterra filters (PAVFs) with a two-layer structure constitute a class of good low-complexity filters. They can efficiently reduce the computational complexity of the conventional adaptive Volterra filter. Their major drawbacks are low convergence rate and high steady-state error caused by the coupling effect between the two layers. In order to remove the coupling effect and improve the performance of PAVFs, we present a novel hierarchical pipelined adaptive Volterra filter (HPAVF)-based alternative update mechanism. The HPAVFs with hierarchical decoupled normalized least mean square (HDNLMS) algorithms are derived to adaptively update weights of its nonlinear and linear subsections. The computational complexity of HPAVF is also analyzed. Simulations of nonlinear system adaptive identification, nonlinear channel equalization, and speech prediction show that the proposed HPAVF with different independent weight vectors in nonlinear subsection has superior performance to conventional Volterra filters, diagonally truncated Volterra filters, and PAVFs in terms of initial convergence, steady-state error, and computational complexity.     

Efficient Acoustic Echo Cancellation With Reduced-Rank Adaptive Filtering Based on Selective Decimation and Adaptive Interpolation

This paper presents a new approach to efficient acoustic echo cancellation (AEC) based on reduced-rank adaptive filtering equipped with selective-decimation and adaptive interpolation. We propose a novel structure of an AEC scheme that jointly optimizes an interpolation filter, a decimation unit, and a reduced-rank filter. With a practical choice of parameters in AEC, the total computational complexity of the proposed reduced-rank scheme with the normalized least mean square (NLMS) algorithm is approximately half of that of the full-rank NLMS algorithm. We discuss the convergence properties of the proposed scheme and present a convergence condition. First, we examine the performance of the proposed scheme in a single-talk situation with an error-minimization criterion adopted in the decimation selection. Second, we investigate the potential of the proposed scheme in a double-talk situation by employing an ideal decimation selection. In addition to mean squared error (MSE) and power spectrum analysis of the echo estimation error, subjective assessments based on absolute category rating are performed, and the results demonstrate that the proposed structure provides significant improvements compared to the full-rank NLMS algorithm.