Hybrid LMS-LMF algorithm for adaptive echo cancellation

The coefficients of an echo canceller with a near-end section and a far-end section are usually updated with the same updating scheme, such as the LMS algorithm. A novel scheme is proposed for echo cancellation that is based on the minimisation of two different cost functions, i.e. one for the near-end section and a different one for the far-end section. The approach considered leads to a substantial improvement in performance over the LMS algorithm when it is applied to both sections of the echo canceller. The convergence properties of the algorithm are derived. The proposed scheme is also shown to be robust to noise variations. Simulation results confirm the superior performance of the new algorithm     

基于频域逐级回归的声学回声控制

传统声学回声控制算法一般采用基于随机梯度法更新的频域分块自适应滤波(PBFDAF)方法,但在以语音为主要回声信号的室内混响环境中,由于回声路径不稳定,往往收敛速度较慢,难以实现足够的回声抑制。该文提出一种基于频域逐级回归的声学回声控制算法。通过逐级回归分析远端信号和麦克风信号之间的线性关系,可以在保持较小的偏差的同时实现收敛较快的系统估计。同时,由于逐级分析了两通道间的短时相干性,因而该算法无需像常见方法一样,额外进行基于通道间相干函数的残余回声抑制或双讲检测,从而保持系统的紧凑性。若进一步假定近端背景噪声准平稳,则可利用基于近端信号非平稳程度的自适应平滑因子,在实现系统估计快速收敛的同时确保其稳定性。实验表明,该方法在常见的近端环境噪声水平下,在收敛速度和稳态误差上相对传统方法有显著优势,非常适合应用在室内远讲模式下的声学回声控制中。     

一种基于软判决的立体声回声抵消算法

提出了一种新的基于维纳滤波的频域算法来解决立体声回声抵消问题,该算法不需要对立体声信号预处理,从而最大程度地保证了近端语音质量。并且它具有很好的鲁棒性,很快的收敛速度和跟踪速度,因而具有一定的实用价值。引入了语音增强中的软判决方法来进一步提高算法的性能。新的算法在保证近端语音质量的同时达到了更好的回声压制效果。仿真实验证明了新算法的良好性能。     

不需双端检测的声回波对消算法研究

声回波对消中双端对讲情况下的近端话音对自适应算法有很大影响。为避免双端话音检测,在滤波型LMS算法基础上,用远端信号和误差输出信号的和代替远端信号去激励预测误差滤波器,降低近端话音的影响。另为进一步提高算法抗近端干扰的能力,做了变步长的改进,首先将步长反比于输出信号预测误差的短时功率,其次将步长正比于预测误差的互相关系数。实验表明,文中提出的两算法在近端话音出现时表现出较好的性能,其中第二种有更好的稳态失调。     

Double-Talk-Robust Prediction Error Identification Algorithms for Acoustic Echo Cancellation

The performance of an acoustic echo canceller may be severely degraded by the presence of a near-end signal. In such a double-talk situation, the variance of the echo path estimate typically increases, resulting in slow convergence or even divergence of the adaptive filter. This problem is usually tackled by equipping the echo canceller with a double-talk detector that freezes adaptation during near-end activity. Nevertheless, there is a need for more robust adaptive algorithms since the adaptive filter's convergence may be affected considerably in the time interval needed to detect double-talk. Moreover, in some applications, near-end noise may be continuously present and then the use of a double-talk detector becomes futile. Robustness to double-talk may be established by taking into account the near-end signal characteristics, which are, however, unknown and time varying. In this paper, we show how concurrent estimation of the echo path and an autoregressive near-end signal model can be performed using prediction error (PE) identification techniques. We develop a general recursive prediction error (RPE) identification algorithm and compare it to three existing algorithms from adaptive feedback cancellation. The potential benefit of the algorithms in a double-talk situation is illustrated by means of computer simulations. It appears that especially in the stochastic gradient case a huge improvement in convergence behavior can be obtained     

Residual echo signal in critically sampled subband acoustic echocancellers based on IIR and FIR filter banks

The residual echo signal characteristics of critically sampled subband acoustic echo cancellers are analyzed. For finite impulse response (FIR) filter banks, the residual echo signal usually has a relatively broad spectral nature around the subband edges. The residual echo signal of power symmetric infinite impulse response (PS-IIR) filter banks, on the other hand, has very narrowband spectral components around the subband edges. These components can be efficiently removed with PS-IIR notch filters that integrate neatly into the filter banks without introducing perceptually noticeable degradation to the near-end speech. This solution has very low computational complexity and does not impinge on the system performance. Simulation studies with recordings from the cockpit of a car, based on a fast QR least-squares adaptive algorithm, demonstrate the potential of this approach for a practical AEC system     

A Signal Decorrelation PNLMS Algorithm for Double-Talk Acoustic Echo Cancellation

Acoustic echo cancellation (AEC) in voiced communication systems is used to eliminate the echo which corrupts the speech signal and reduces the efficiency of signal transmission. Usually, the performance of AEC system based on the adaptive filtering degrades seriously in the presence of speech issued from the near-end speaker (double-talk). In typical AEC scenarios, double-talk detector (DTD) must be added to AEC for improving speech quality. One of the main problems in AEC with DTD is that the DTD errors can result in either large residual echo or distorting the near-end input speech. Considering the strong correlation property of speech signals, this paper presents a novel proportionate decorrelation normalized least-mean-square (PDNLMS) adaptive AEC without DTD for echo cancellation as an interesting alternative to the typical AEC with DTDs. Unlike traditional AEC with a DTD, the proposed PDNLMS uses the difference of near-end speech as the residual error to update adaptive echo channel filter during the periods of double-talk, which can efficiently reduce the double-talk influence on the AEC adaptation process. The experimental results show that not only the proposed PDNLMS without DTD illustrate better stability and faster convergence rate, but it is also of a lower steady-state misalignment and better residual signal than current methods with DTDs at a lower computational cost.     

A multirate acoustic echo canceler structure

A new subband echo canceler (SBEC) structure is proposed to reduce the transmission delay introduced by conventional SBEC structures, without distorting the near-end signal. The proposed structure is based on computing two output errors, one for using during single-talk and the other one for using during double-talk periods. With the SBEC structure we propose a double-talk detector with a subband configuration which allows a fast and accurate detection of double-talk periods, enabling the SBEC algorithm to track changes in the echo path impulse response when the near-end signal is absent. Computer simulations using actual speech signals, and subjective evaluation tests are given to show the convergence performance, tracking and double-talk detection ability, of the proposed scheme     

应用Hopfield神经网络抑制高频地波雷达中短波通信干扰

首先分析混频输入级高频地波雷达中短波通信干扰的特征,借鉴用基于LMS算法的自适应滤波方法抑制短波通信干扰的处理思想,通过构建基于Hopfield神经网络的自适应滤波器来抑制短波通信干扰.通过仿真比较这两种自适应滤波器的处理效果,验证了用基于Hopfield神经网络的自适应滤波来抑制短波通信干扰方案是可行的.     

Algorithm of Robust Frequency Estimation in a Channel with White Gaussian Noise and Pulse Interferences

An algorithm for robust frequency estimation in a channel with additive white Gaussian noise and pulse interference is proposed. The algorithm involves iterative elimination of pulse interferences and subsequent calculation of the maximum likelihood estimate. It is demonstrated that the proposed filter-cleaner differs from the well-known Martin–Thomson filter in the functional dependence of adaptive coefficients on the estimates of parameters of distributions of the linear prediction error and the amplitude envelope of the signal. The results of mathematical modeling and numerical estimation of the threshold point of the method (0.49) are presented.     

基于递归最小二乘算法的时相调制频移滤波方法

针对噪声干扰,利用时相调制信号的谱相关特性,采用非平稳信号处理方法,提出一种基于递归最小二乘算法的时相调制频移滤波方法;同时对具有循环平稳特性的干扰源抑制方法进行了讨论,并分别给出相应的滤波器框图;对采用该滤波器前后的时相调制系统性能进行了仿真与比较。仿真结果表明:与循环相关匹配滤波方法相比,基于最小二乘算法的时相调制频移滤波方法可以有效抑制噪声对时相调制系统的影响,在满足10-5的误码率条件下,系统的功率需求降低约3dB.     

基于最小范数准则的多普勒频率高精度估计

提出一种雷达回波多普勒频率的高精度估计算法。算法利用脉冲间干扰和噪声的相关性较弱,从而在脉冲链自相关矩阵中抑制干扰及噪声分量。并基于最小范数准则实现多普勒频率估计。该算法在多种干扰/噪声背景下估计精度接近CRB,且计算量较小。仿真实验也验证了算法的有效性。     

基于盲信号分离的自适应回声抵消算法

在视频会议和免提通信系统中,扬声器和麦克风之间的声耦合严重影响语音通信系统的质量。文中提出了一种用基于盲信号分离（BSS）的自适应声回波对消（AEC）方法,可有效解决回声和噪声对近端语音信号的影响。该方法不仅能减少背景噪声对回波对消的影响,而在双向通话时,可直接利用盲信号分离技术分离出近端语音。     

Adaptive time-varying cancellation of wideband interferences inspread-spectrum communications based on time-frequencydistributions

The paper proposes an adaptive method for suppressing wideband interferences in spread-spectrum (SS) communications. The proposed method is based on the time-frequency representation of the received signal from which the parameters of an adaptive time-varying interference excision filter are estimated. The approach is based on the generalized Wigner-Hough transform as an effective way to estimate the instantaneous frequency of parametric signals embedded in noise. The performance of the proposed approach is evaluated in the presence of linear and sinusoidal FM interferences plus white Gaussian noise in terms of the SNR improvement factor and bit error rate (BER)     

A new configuration for echo canceller adaptable during double talkperiods

A three-port echo canceler (EC) configuration is proposed which observes the signal of the near-end side on a two-wire circuit in addition to the four-wire circuit signals. Embedding these signals on hybrid ports into a three-dimensional autoregressive process, echo path and innovations of near- and far-end speeches can be estimated through a three-channel lattice filter. The new configuration is then able to track echo path time variance, even during double talk (DT), and requires no changeover at either the beginning or end of DT, thus eliminating the need for DT detection. Two echo synthesizers utilizing inverse lattice and the echo path estimate possess guaranteed stability without the need for testing     

Gabor expansion for adaptive echo cancellation

A good echo cancellation algorithm should have a fast convergence rate, small steady-state residual echo, and less implementation cost. The normalized least mean square (NLMS) adaptive filtering algorithm may not achieve this goal. We show that using the Gabor expansion is a way to achieve this goal. For direct digital signal processing compatibility the Gabor expansion introduced in this paper is for discrete-time signals, although the Gabor expansion also can be used for continuous-time signals. The Gabor expansion can be defined as a discrete-time signal representation in the joint time-frequency domain of a weighted sum of the collection of functions (known as the synthesis functions). There are several design issues in the echo canceller based on the Gabor expansion: the design of the analysis functions for the far-end speech, the design of the analysis functions for the near-end signal containing the echo plus the near-end speech, the design of the adaptive filters in the subsignal path, and the design of the synthesis functions. All the adaptive filters are designed using identical NLMS adaptive filtering algorithms     

LMS-LMF adaptive scheme for echo cancellation

The authors present a novel algorithm for echo cancellation. The algorithm consists of simultaneously applying the LMS algorithm to the near-end section of the echo canceller, and a controlled mixed LMS-LMF algorithm to the far-end section. This combination results in a substantial improvement in performance of the proposed scheme over the LMS and the LMF algorithms     

Frequency-domain blind deconvolution based on mutual information rate

In this paper, a new blind single-input single-output (SISO) deconvolution method based on the minimization of the mutual information rate of the deconvolved output is proposed. The method works in the frequency domain and requires estimation of the signal probability density function. Thus, the algorithm uses higher order statistics (except for Gaussian source) and allows non-minimum-phase filter estimation. In practice, the criterion contains a regularization term for limiting noise amplification as in Wiener filtering. The score function estimation, which represents a key point of the algorithm, is detailed, and the most robust estimate is selected. Finally, experiments point to the relevance of the proposed algorithm: 1) any filter, minimum phase or not, can be estimated and 2) on actual data (underwater explosions, seismovolcanic phenomena), this deconvolution algorithm provides good results with a better tradeoff between deconvolution quality and noise amplification than existing methods.     

A New Robust Variable Step-Size NLMS Algorithm

A new framework for designing robust adaptive filters is introduced. It is based on the optimization of a certain cost function subject to a time-dependent constraint on the norm of the filter update. Particularly, we present a robust variable step-size NLMS algorithm which optimizes the square of the a posteriori error. We also show the link between the proposed algorithm and another one derived using a robust statistics approach. In addition, a theoretical model for predicting the transient and steady-state behavior and a proof of almost sure filter convergence are provided. The algorithm is then tested in different environments for system identification and acoustic echo cancelation applications.     

基于压缩感知的脉间捷变频SAR成像研究

捷变频技术应用到合成孔径雷达系统中会存在多普勒调频率捷变、方位向无法压缩等问题.在分析捷变频SAR回波相位特性的基础上,本文研究了传统的相关法在方位压缩中的应用;针对相关法效率低、旁瓣高等固有缺陷,考虑到回波信号的稀疏性,提出了基于压缩感知的方位压缩算法,并形成了一种距离压缩采用匹配滤波、方位压缩采用压缩感知的捷变频SAR二维成像方案.仿真实验表明,该方案能克服多普勒调频率捷变等问题,实现捷变频SAR二维成像,并具有低旁瓣、高分辨等优点.