基于双传声器的蓝牙耳机降噪算法

用于免提通信设备的语音增强算法一直是研究的热点问题,而算法处理结果的音质问题近年来也备受关注。针对基于双传声器降噪的蓝牙耳机系统,将常用多通道传声器降噪算法归纳为基于相干函数法和基于空间预分离法这两大类进行分析和比较。基于相干函数法利用两个通道间信号的相干函数对含噪信号滤波达到降噪目的,而基于空间预分离法利用空间特性从含噪信号中分离出噪声参考信号来消除噪声。分析基于降噪量、语音音质和综合性能三个指标,从约束语音损伤的角度分析最优解的形式,并对比两类算法的实际性能。结果表明选择合适的算法可权衡降噪量与语音损伤,达到较好的综合性能。     

一种自适应变阶谱减降噪算法在电子耳蜗中的应用

由于传统谱减语音增强存在残留的"音乐噪声",因此基于传统谱减法降噪的电子耳蜗(CI)感知的声音品质也会受到影响.为提高CI的抗噪性,本文提出了一种自适应变阶谱减算法,并将该方法应用于电子耳蜗的语音增强中.根据CI电极对应的频带关系,该算法先对采集的带噪声音信号功率谱进行Bark子带划分,并在每个Bark子带中根据信噪比的变化进行谱减阶数和系数的自适应调节,使各子带噪声更均衡地去除,基本消除了传统方法存在的"音乐噪声".基于该算法的电子耳蜗ACE仿真实验及测听结果表明,与传统谱减法相比,改进的算法能更好地抑制背景噪声和残留噪声,仿真得到的CI合成音感知更好和更清晰.     

时频结合自适应阈值小波包消噪算法

在充分考虑人耳听觉特性和噪声统计特性的基础上,提出一种时频结合Bark尺度自适应阈值的语音消噪算法,在Bark频域上自适应调整增强系数可以较准确地进行阈值判定。仿真实验验证,时频结合算法在低信噪比输入情况下较传统语音降噪方法具有明显优势,其在消除高斯白噪声的同时有效降低了语音损失,可获得最大信噪比,谱失真测度最小,增强语音的MOS(Mean Opinion Score)评分明显提高,具有较好的听觉效果。     

语音识别预处理中的ICA噪声适应性分析

针对高噪环境下语音识别的困难,提出一种基于独立分量分析的盲分离(ICA/BSS)与小波联合的语音降噪预处理方法,针对不同种类和不同输入信噪比的噪声设计了试验,结果表明基于ICA的语音识别预处理方法对低输入信噪比情况下多种噪声具有很强的鲁棒性和优越性,此结论对现实世界高噪环境下的信号分析和语音识别具有重要意义。     

一种用于主动降噪耳机的权重滤波误差信号滤波-x最小均方算法*

针对传统FxLMS算法前馈自适应主动降噪耳机系统因果性条件不足时在宽带噪声环境中产生的高频噪声抬升问题,该文引入权重滤波误差信号FxLMS算法用于抑制高频噪声的抬升,但该算法带来了低频降噪量不足问题。因此,进一步提出将固定系数混合控制器与权重滤波误差信号FxLMS算法结合,在解决高频噪声抬升问题的同时,保证了良好的低频降噪量。基于DSP平台实现了提出的主动降噪耳机方案。实验证明,该方案针对宽带和单频等噪声都取得了较好的降噪效果。     

非线性时间序列的小波分频预测

基于噪声的小波变换特点，结合小波包分解和模极大重构来抽取含噪信号的主分量，提出了一种基于最佳尺度分解和Volterra自适应滤波的分频预测算法，使用较少的模型训练样本，同时具有强的抗噪能力.该算法克服了传统小波分解尺度选取的盲目性及单纯Volterra预测器抗噪性能的不足，数值仿真表明，针对含强噪声的非线性信号可进行有效预测. 关键词： 小波分解 Volterra自适应滤波器 分频预测     

一种强噪声背景下微弱超声信号提取方法研究

为解决在强噪声背景下获取超声信号的难题,基于粒子群优化算法和稀疏分解理论提出一种强噪声背景下微弱超声信号提取方法.该方法将降噪问题转换为在无穷大参数集上对函数进行优化的问题,首先以稀疏分解理论和超声信号的结构特点为依据构建了粒子群优化算法运行所需要的目标函数及去噪后信号的重构函数,从而将粒子群优化算法和超声信号降噪联系在一起;然后根据粒子群优化算法可以在连续参数空间寻优的特点建立了用于匹配超声信号的连续超完备字典,并采用改进的自适应粒子群优化算法在该字典中对目标函数进行优化;最后根据对目标函数在字典上的优化结果确定最优原子,并利用最优原子按照重构函数重构出降噪后的超声信号.通过对仿真超声信号和实测超声信号的处理,结果表明本文提出的方法可以有效提取信噪比低至-4 dB的强噪声背景下的微弱超声信号,且和基于自适应阈值的小波方法相比本文方法表现出更好的降噪性能.     

互补型自适应滤波器在心磁信号处理中的应用

将心磁信号从干扰噪声中加以提取并有效地消除噪声干扰是心磁信号处理中尤为重要的环节 .从改进算法的角度出发，提出互补型自适应滤波器结构以实现心磁信号的消噪处理.该滤波器针对心磁这类非平稳信号进行设计，有效地解决了常规自适应滤波器应用于心磁信号处理时收敛速度和稳态误差的矛盾.通过仿真实验和心磁实验结果表明，该算法能有效地消除心磁信号的背景噪声和工频干扰噪声.同时该算法也可用于其他非平稳信号的消噪处理. 关键词： 自适应滤波 心磁图 最小均方误差     

时变带限信道中光通信的均衡与去噪技术

为了解决时变带限信道中的光通信问题,基于自适应均衡和盲均衡方法,提出了一种半盲光信号均衡方案,适用于大气海洋等时变带限光信道的光通信信号处理.对带限光信道中传输的包含泊松噪音开关键控调制光脉冲信号的不同数字均衡处理方法进行了数值模拟,比较了不同方法的结果,证明半盲均衡能更快匹配光信道且能保持收敛.同时,利用小波方法研究了含噪光接收信号去噪问题,发现光滑小波软阈值去噪方法更适合空间光脉冲信号波形,并对通信眼图进行了去噪的数值模拟.     

基于自适应阈值的三维点云分段式去噪方法

激光雷达探测点云数据中存在大量噪声点,导致三维图重建精度下降,无法完全复现物体结构,本文针对此问题提出了一种基于自适应阈值的三维点云分段式去噪方法.根据噪声点与非噪声点之间的欧式距离,将其划分为远信号噪声点和近信号噪声点两类,先后对两类噪声点分别采用基于非线性函数的阈值自适应去噪算法和基于曲率的去噪算法.基于非线性函数...     

A wavelet-based forward BSS algorithm for acoustic noise reduction and speech enhancement

In this paper, we address the problem of noise reduction and speech enhancement by adaptive filtering algorithm. Recently, the well known forward blind source separation (FBSS) structure has been largely studied and intensively used to reduce acoustic noise components and to enhance speech signal. The FBSS structure is often combined with adaptive algorithms to accelerate the adaptation of the cross-filters, and to improve noise suppression at the output. In this paper, we propose to use a wavelet transform decomposition in the FBSS structure by using a two-channel forward wavelet symmetric adaptive decorrelating (WFSAD) algorithm. The proposed WFSAD algorithm provides a better compromise between time and frequency resolution and improves robustness of the noise reduction process when compared with the classical two-channel forward symmetric adaptive decorrelating (FSAD) algorithm. Simulation results prove the efficiency of the proposed WFBSS algorithm in comparison with conventional ones in terms of several objective and subjective criteria.     

听觉频域掩蔽效应的自适应β阶贝叶斯感知估计语音增强算法

为了在噪声抑制和语音失真中之间寻找最佳平衡,提出了一种听觉频域掩蔽效应的自适应β阶贝叶斯感知估计语音增强算法,以期提高语音增强的综合性能。算法利用了人耳的听觉掩蔽效应,根据计算得到的频域掩蔽阈自适应调整β阶贝叶斯感知估计语音增强算法中的β值,从而仅将噪声抑制在掩蔽阈之下,保留较多的语音信息,降低语音失真。并分别用客观和主观评价方式,对所提出的算法的性能进行了评估,并与原来基于信噪比的自适应β阶贝叶斯感知估计语音增强算法进行了比较。结果表明,频域掩蔽的β阶贝叶斯感知估计方法的综合客观评价结果在信噪比为-10 dB至5 dB之间时均高于基于信噪比的自适应β阶贝叶斯感知估计语音增强算法。主观评价结果也表明频域掩蔽的β阶贝叶斯感知估计方法能在尽量保留语音信息的同时,较好的抑制背景噪声。      

A constant modulus algorithm for blind equalization in α-stable noise

Channel noise is often assumed to be Gaussian in most of the existing channel equalization algorithms. The performance of these algorithms will degrade seriously when the noise is non-Gaussian. This paper deals with the problem of blind channel equalization in impulsive noise environment that is modeled as α-stable process. A modified adaptive error-constrained constant modulus algorithm (MAECCMA) is proposed by soft-limiting the amplitude of the equalizer input and transforming the error signal of the original adaptive error-constrained constant modulus algorithm (AECCMA) nonlinearly to suppress the influence of α-stable noise. Computer simulation results of two underwater acoustic channels show that, MAECCMA has almost the same performance as AECCMA and they both have faster convergence rate than constant modulus algorithm (CMA) and normalized least mean absolute deviation (NLMAD) algorithm in Gaussian noise, while MAECCMA provides the best performance of those four algorithms in α-stable noise.     

Improved Approach for the Maximum Entropy Deconvolution Problem

The probability density function (pdf) valid for the Gaussian case is often applied for describing the convolutional noise pdf in the blind adaptive deconvolution problem, although it is known that it can be applied only at the latter stages of the deconvolution process, where the convolutional noise pdf tends to be approximately Gaussian. Recently, the deconvolutional noise pdf was approximated with the Edgeworth Expansion and with the Maximum Entropy density function for the 16 Quadrature Amplitude Modulation (QAM) input but no equalization performance improvement was seen for the hard channel case with the equalization algorithm based on the Maximum Entropy density function approach for the convolutional noise pdf compared with the original Maximum Entropy algorithm, while for the Edgeworth Expansion approximation technique, additional predefined parameters were needed in the algorithm. In this paper, the Generalized Gaussian density (GGD) function and the Edgeworth Expansion are applied for approximating the convolutional noise pdf for the 16 QAM input case, with no need for additional predefined parameters in the obtained equalization method. Simulation results indicate that improved equalization performance is obtained from the convergence time point of view of approximately 15,000 symbols for the hard channel case with our new proposed equalization method based on the new model for the convolutional noise pdf compared to the original Maximum Entropy algorithm. By convergence time, we mean the number of symbols required to reach a residual inter-symbol-interference (ISI) for which reliable decisions can be made on the equalized output sequence.     

小波包自适应阈值语音降噪新算法

为了克服低信噪比输入下,语音增强造成语音清音中的弱分量损失,造成重构信号包络失真的问题。论文提出了一种新的语音增强方法。该方法根据语音感知模型,采用不完全小波包分解拟合语音临界频带,并对语音按子带能量进行清浊音区分处理,在阈值计算上,提出了一种清浊音分离,基于子带信号能量的小波包自适应阈值算法。通过仿真实验,客观评测和听音测试表明,该算法在低信噪比输入时较传统算法,能够更加有效地减少重构信号包络失真,在不损伤语音清晰度和自然度的前提下,使输出信噪比明显提高。将该算法与能量谱减法结合,进行二次增强能进一步提高降噪输出的语音质量。     

A new regularized forward blind source separation algorithm for automatic speech quality enhancement

This paper addresses the problem of speech enhancement and acoustic noise reduction by adaptive filtering algorithms in a moving car through blind source separation (BSS) structures. In this paper we propose a new regularized forward blind source separation (RFBSS) algorithm that does not need voice activity detection (VAD) systems, and allows getting efficient speech enhancement performances with low complexity.     

Robust Blind Adaptive Channel Equalization in Chaotic Communication Systems

Based on the bounded property and statistics of chaotic signal and the idea of set-membership identification, we propose a set-membership generalized least mean square （SM-GLMS） algorithm with variable step size for blind adaptive channel equalization in chaotic communication systems. The steady state performance of the proposed SM-GLMS algorithm is analysed, and comparison with an extended Kalman filter （EKF）-based adaptive algorithm and variable gain least mean square （VG-LMS） algorithm is performed for blind adaptive channel equalization. Simulations show that the proposed SM-GLMS algorithm can provide more significant steady state performance improvement than the EKF-based adaptive algorithm and VG-LMS algorithm.