基于小波旁瓣相消器的轴承故障特征提取

为解决强背景噪声下声信号提取的轴承故障特征不显著问题,提出一种基于小波旁瓣相消器的故障特征提取方法。该方法利用小波滤波器组将含噪故障轴承声信号变换到小波域,进行小波域阵列广义旁瓣相消自适应波束形成,再通过小波滤波器组重构增强后的故障轴承信号,最后对重构增强后的信号进行包络解调并提取故障特征频率进行故障诊断。实验结果表明,该方法能够在强背景噪声下有效提取滚动轴承故障特征,并且相较于传统的延时求和波束形成器具有更好的降噪和故障特征增强效果。     

基于自适应仿生小波变换的语音增强方法

分析自适应滤波和小波滤波的原理与方法,提出一种基于自适应仿生小波变换的语音增强方法.该方法首先用仿生小波变换对含噪语音信号进行小波分解,这样可以保证对信号频率和幅值的听觉特性,然后将经仿生小波变换所分离出来的噪声成分作为自适应滤波器的输入.通过选用自适应滤波器的最小二乘算法(RLS)从而实现信噪分离的最佳滤波,以保证去除信号中的相关噪声.实验结果表明,该方法对语音信号有显著的增强效果,能实现语音信号在同频段对噪声成分和有用信号的最佳估计.     

基于噪声抵消与波束形成的小阵语音增强

为了提高麦克风小阵的语音增强效果,将阵列抗串扰自适应噪声抵消（ACRANC）方法与波束形成（BF）方法相结合,提出了一种效果更好的小阵语音增强方法。首先,通过生成多个ACRANC子系统,获得多路增强语音信号;然后,通过所提的自适应模式控制（AMC）算法和延迟求和（DAS）波束形成方法进一步提高多路增强语音信号的增强效果。对提出的方法进行了计算复杂度估计,验证了所提方法可以通过普通芯片实时实现。实际环境下的实验结果也表明,所提方法的语音增强效果相较ACRANC方法有所提高,具有一定的优越性。     

自适应语音分段增强算法

近年来小波理论在信号分析、处理中得到了广泛的应用,本文提出了一种自适应分段小波域语音增强(ASWE)算法,即采用局部余弦包变换对语音信号自适应分段,然后对每一段语音采用基于小波变换的语音增强处理该方法不需要噪声的先验知识,且适合于缓慢变化的非平稳噪声.最后的仿真实验表明,该方法比直接用小波去噪效果好,是一种有效的语音增强技术     

基于鲁棒波束形成的麦克风阵列系统

为了提高噪声消除的鲁棒性,传统的通用旁瓣消除器采用固定波束形成作为输出的系数约束自适应滤波器,采用CCAF的输出作为输入的标准约束自适应滤波器.此系统的跟踪性能随信号频率变化而改变,并且由于波束带宽的影响使得目标语音在低频发生畸变.采用优化波束形成方法来得到较宽的带宽,对输出进行后滤波处理来消除残余噪声.试验表明本方法可明显提高语音信噪比.     

基于麦克风阵列的语音增强研究

麦克风阵列的自适应算法通过迭代运算获取波束形成的最优权矢量时,噪声模型的估计是一个非常关键的因素.它的好坏直接影响着系统波束形成的性能.系统地分析了最小均方(LMS)自适应语音增强算法,并针对阻塞矩阵在估计噪声时存在的缺陷,在该算法的基础上提出了一种利用最小值控制递归平均(MCRA)来估计噪声的方法.将此方法应用于波束形成,并用 Matlab 软件进行仿真.仿真实验结果表明,MCRA 估计出的噪声使 LMS 自适应语音增强的效果更好和抗噪性更强.     

稳键的自适应空间波束形成语音增强算法研究

为了进一步改善波束形成的降噪性能,研究了一种稳键后置滤波自适应空间波束形成算法.用麦克风代替传统波束形成器的延时抽头线,使所有的麦克风都有一阶的滤波器,利用经典的线性约束最小方差准则使空间波束形成产生语音参考信号,同阻塞矩阵输出的噪声参考信号一起经自适应多路相消器,从而有效的消除干扰噪声;最后结合后置滤波技术进一步改善语音质量.实验结果表明,相对于传统后置滤波自适应波束形成算法,在消噪性能上有明显的改善且具有更高的输出信噪比.     

基于麦克风阵列的语音增强技术及应用

用麦克风阵列进行语音处理的方法可以提高信噪比,解决环境噪声、回声和混响引起的语音识别性能降低的问题.介绍基于延迟-累加方法(传统波束法) 、自适应波束法及基于后置自适应滤波等结构的麦克风阵列语音增强的基本原理,总结了各种算法的特点.     

改进的辅助变换相干干扰抑制波束形成技术

提出一种相干干扰下自适应波束形成方法。该方法同时约束非相干干扰与相干干扰,更好地实现对此两类干扰方向的自适应零陷滤波。采用变换预处理,避免抑制期望信号;恢复噪声特性并采用对角加载技术,增强算法稳健性;采用拉格朗日乘子法得到最优波束形成算法。理论分析与仿真结果表明,保持阵列孔径不变时,该算法提高了输出信干噪比和稳健性。     

线列阵高分辨方位估计方法研究

针对水声设备的实际应用,给出了基于实测基阵阵列流形的波束域高分辨方位估计的MUSIC方法.在预形成多波束时,该方法采用适用于任意阵形的自适应波束优化技术,针对水下实际阵列设计超低旁瓣波束以抑制水面上分布的强干扰.为了减小实际阵列模型和理想阵列模型失配所带来的不利影响,在多波束设计时,利用实测阵列流形代替理想阵列流形进行设计,消声水池实验结果验证了该方法可以有效地降低系统中由于阵列模型失配所造成的波束域MUSIC方位估计方法的性能损失.     

基于正交小波变换的自适应语音消噪改进方法

提出一种基于正交小波变换的自适应语音消噪改进方法，这种方法可以提高自适应语音消噪过程的收敛速率．正交小波变换在自适应滤波中起到白化的作用，使自适应滤波器的输入正交化．通过正交小波分解，自适应滤波器中的信号能量降低，输入自相关矩阵的动态范围减小，特征值分布更加集中，从而使收敛速率加快．     

An adaptive speech source separation algorithm under overcomplete-cases using Laplacian mixture modeling for mixture matrix estimation by adaptive EM-type algorithm in wavelet packet domain

Speech process has benefited a great deal from the wavelet transforms. Wavelet packets decompose signals in to broader components using linear spectral bisecting. In this paper, mixtures of speech signals are decomposed using wavelet packets, the phase difference between the two mixtures are investigated in wavelet domain. In our method Laplacian Mixture Model (LMM) is defined. An Expectation Maximization (EM) algorithm is used for training of the model and calculation of model parameters which is the mixture matrix. And then we compare estimation of mixing matrix by LMM-EM with different wavelets. And then we use adaptive algorithm in each wavelet packet for speech separation and we see better results are obtained. Therefore individual speech components of speech mixtures are separated.     

An expert system for speaker identification using adaptive wavelet sure entropy

In this study, an expert speaker identification system is presented for speaker identification using Turkish speech signals. Here, a discrete wavelet adaptive network based fuzzy inference system (DWANFIS) model is used for this aim. This model consists of two layers: discrete wavelet and adaptive network based fuzzy inference system. The discrete wavelet layer is used for adaptive feature extraction in the time–frequency domain and is composed of discrete wavelet decomposition and discrete wavelet entropy. The performance of the used system is evaluated by using repeated speech signals. These test results show the effectiveness of the developed intelligent system presented in this paper. The rate of correct classification is about 90.55% for the sample speakers.     

The speaker identification by using genetic wavelet adaptive network based fuzzy inference system

In this paper, an intelligent speaker identification system is presented for speaker identification by using speech/voice signal. This study includes both combination of the adaptive feature extraction and classification by using optimum wavelet entropy parameter values. These optimum wavelet entropy values are obtained from measured Turkish speech/voice signal waveforms using speech experimental set. It is developed a genetic wavelet adaptive network based on fuzzy inference system (GWANFIS) model in this study. This model consists of three layers which are genetic algorithm, wavelet and adaptive network based on fuzzy inference system (ANFIS). The genetic algorithm layer is used for selecting of the feature extraction method and obtaining the optimum wavelet entropy parameter values. In this study, one of the eight different feature extraction methods is selected by using genetic algorithm. Alternative feature extraction methods are wavelet decomposition, wavelet decomposition – short time Fourier transform, wavelet decomposition – Born–Jordan time–frequency representation, wavelet decomposition – Choi–Williams time–frequency representation, wavelet decomposition – Margenau–Hill time–frequency representation, wavelet decomposition – Wigner–Ville time–frequency representation, wavelet decomposition – Page time–frequency representation, wavelet decomposition – Zhao–Atlas–Marks time–frequency representation. The wavelet layer is used for optimum feature extraction in the time–frequency domain and is composed of wavelet decomposition and wavelet entropies. The ANFIS approach is used for evaluating to fitness function of the genetic algorithm and for classification speakers. It has been evaluated the performance of the developed system by using noisy Turkish speech/voice signals. The test results showed that this system is effective in detecting real speech signals. The correct classification rate is about 91% for speaker classification.     

基于阈值的小波域语音降噪新算法

提出了一种新的基于阈值的小波域语音降噪算法。采用小波包对含噪语音进行分解，克服了传统的正交小波变换的缺陷。采用自适应阈值的方法，对每一尺度上的噪声最大量进行去噪，保留有用信号，可以进一步提高信噪比，仿真实验表明，该方法有更好的去噪效果。     

基于子带分解的DFRFT自适应滤波语音增强算法

提出一种改进的语音增强方法,利用子带分解对带噪语音信号进行处理,再在离散分数傅里叶变换(DFRFT)域采用最小均方(LMs)自适应算法进行滤波,对滤波后的子带信号进行DFRFT逆变换,最后利用综合滤波器组合成增强后的语音信号。仿真结果表明,本算法明显提高了收敛速度,减少了计算时间。在主客观评价中均具有较好的语音增强效果。     

一种有效的实时基音检测算法

针对基音周期检测实时性的要求,提出了基于小波变换的实时语音基音周期检测算法。该算法利用小波变换极值与信号突变点之间的关系,将小波域波形与时域波形相结合,采取自适应基准、多特征参数提取小波系数极大值,并在2.5ms时间内捕捉并检测到新的基音脉冲位置。实验表明,该算法对语音和残差信号取得了较好结果。     

基于新阈值函数和自适应阈值的小波包语音增强研究

针对传统的小波包语音增强算法增强后的语音失真严重的问题,本文提出了一种基于自适应阈值和新阈值函数的小波包语音增强算法。该算法在小波包域将带噪语音加窗分帧,基于相邻帧快速傅立叶变换功率谱的互相关值,计算各帧存在语音的概率,然后通过语音存在概率对传统通用小波包阈值进行调整,使得阈值在非语音帧中较大,在语音帧中较小,实现阈值的自适应调整,可以在最大程度消除噪声的同时,尽可能的保留语音,减小语音失真。本文还设计了一种新阈值函数,克服了传统硬阈值函数不连续和软阈值函数会带来恒定偏差的缺点,进一步减小了语音失真。本文采用TIMIT 数据库和NOISEX-92 数据库中的语音和噪声进行了大量的模拟实验,主观评比和客观评比结果均证明本文提出的语音增强算法比现有的两种算法有更好的增强效果,采用本文算法增强后的语音失真更小,听觉效果更好。     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.