基于MELMD-ICA的光纤振动信号降噪方法

针对分布式光纤传感系统所采集含噪信号,提出一种改进集成局部均值分解(MELMD)联合独立成分分析(ICA)的降噪方法,引入排列熵判决机制提高抑制模态混叠与虚假分量能力。首先使用MELMD方法分解含噪信号得到乘积函数(PF)并进行信号重构;将含噪信号和重构信号求差得到虚拟噪声,构造虚拟通道;然后使用ICA对含噪信号和虚拟通道进行信噪分离,得到最终结果。通过实验验证,该方法与EMD-ICA,EEMD-ICA,MELMD相比,能更好地消除信号中的噪声,保留信号的特征信息。     

改进语音增强多频带谱减算法研究

针对传统谱减语音增强算法增强后的语音信号会残留明显的"音乐噪声"的问题,采用多频带谱减算法对其进行改进。改进算法的原理是将带噪的语音信号按照频率划分成不同的频带,并使这些频带之间互不交叠,根据频带内带有噪声的语音信号和噪声信号信噪比,利用自适应算法求得该频带的过减因子。仿真结果表明:改进多频带谱减算法的语音增强效果优于传统谱减法。     

语音增强技术在MELP声码器中的应用

为了使得MELP声码器在高噪声环境下仍然获得较好的语音效果,需对含噪声语音进行语音增强。本文采用谱减法和独立分量分析相结合方法,对语音进行增强。该方法可以在不增加语音采样硬件的条件下,满足独立分量分析中观测信号的数目不少于源信号数目的约束条件。结果表明,该方法能较好的分离出噪声和语音信号,增强输入到MELP声码器中的语音信号,提高MELP声码器在高噪声环境下应用的语音效果。     

基于短时对数谱估计MMSE的语音增强算法研究

结合语音激活检测(VAD)技术对短时对数谱估计最小均方误差(LSA-MMSE)语音增强算法进行了改进。通过实验表明,LSA-MMSE增强算法在消除背景噪声、增加语音清晰度和提高语音自然度等方面比谱减法更加有效。     

Perceptual speech coding and enhancement using frame-synchronizedfast wavelet packet transform algorithms

This paper presents new wideband speech coding and integrated speech coding-enhancement systems based on frame-synchronized fast wavelet packet transform algorithms. It also formulates temporal and spectral psychoacoustic models of masking adapted to wavelet packet analysis. The algorithm of the proposed FFT-like overlapped block orthogonal wavelet packet transform permits us to efficiently approximate the auditory critical band decomposition in the time and frequency domains. This allows us to make use of the temporal and spectral masking properties of the human auditory system to decrease the average bit rate of the encoder while perceptually hiding the quantization error. The same wavelet packet representation is used to merge speech enhancement and coding in the context of auditory modeling. The advantage of the method presented in this paper over previous approaches is that perceptual enhancement and coding, which is usually implemented as a cascade of two separate systems, are combined. This leads to a decreased computational load. Experiments show that the proposed wideband coding procedure by itself can achieve transparent coding of speech signals sampled at 16 kHz at an average bit rate of 39.4 kbit/s. The combined speech coding-enhancement procedure achieves higher bit rate values that depend on the residual noise characteristics at the output of the enhancement process     

Extracting Epileptic Feature Spikes Using Independent Component Analysis

In recent years, blind source separation （BSS） by independent component analysis （ICA） has been drawing much attention because of its potential applications in signal processing such as in speech recognition systems, telecommunication and medical signal processing. In this paper, two algorithms of independent component analysis （fixed-point IC,4 and natural gradient-flexible ICA） are adopted to extract human epileptic feature spikes from interferential signals. Experiment results show that epileptic spikes can be extracted from noise successfully. The kurtosis of the epileptic component signal separated is much better than that of other noisy signals. It shows that ICA is an effective tool to extract epileptic spikes from patients＇ electroencephalogram EEG and shows promising application to assist physicians to diagnose epilepsy and estimate the epileptogenic region in clinic.     

基于无语音概率改进的对数谱幅度估计增强算法

针对谱减语音增强法中一直存在的去噪度、残留的音乐噪声和语音畸变度三者间均衡这一关键问题,本文提出一种基于无语音概率改进的对数谱估计增强算法.该算法结合无语音概率的思想,按照纯噪声帧和带噪语音帧两种状态.有区别地实时更新语音最小均方误差的对数谱增益,并利用无语音概率参数(SAP)自适应地调节平滑系数,以求随着噪声环境的变化,在去噪度、残留"音乐噪声"和语音畸变度之间自适应地折中.实验表明,该算法在相同去噪程度下,语音畸变和音乐噪声相对其他谱减法都同时地减弱,特别在低信噪比环境下优势更明显,而且平滑参数利用SAP参数,无需多余计算,便于实时处理.     

An Algorithm of Voice Activity Detection Based on EMD and Wavelet Entropy Ratio

A new method was proposed to identify speech-segment endpoints based on the empirical mode decomposition (EMD) and a new wavelet entropy ratio with improving the accuracy of voice activity detection. With the EMD, the noise signals can be decomposed into several intrinsic mode functions (IMFs). Then the proposed wavelet energy entropy ratio can be used to extract the desired feature for each IMFs component. In view of the question that the method of voice endpoint detection based on the original wavelet entropy ratio cannot adapt to the low signal-to-noise ratio (SNR) condition, an appropriate positive constant was introduced to the basic wavelet energy entropy ratio with effectively improved discriminability between the speech and noise. After comparing the traditional wavelet energy entropy ratio with the proposed wavelet energy entropy ratio, the experiment results show that the proposed method is simple and fast. The speech endpoints can be accurately detected in low SNR environments.     

基于最优小波基函数的语音增强研究

小波分析具有时频局部化和多分辨率特性,而小波包分析是在小波分析的基础上对信号高频部分的更精细地分解,选取合适的小波基一直是小波包去噪分析中的关键问题。将熵函数作为选取最优小波基的评价标准,通过计算语音信号小波包分解系数的熵值来确定合适的分解方式,同时采用小波包阈值去噪算法对三种小波基进行小波包去噪仿真实验,并进行对比分析。仿真实验表明,两种熵函数选取的最优小波基都能较好地消除强噪声背景下的噪声,得到信噪比较高的语音信号。     

一种基于自适应噪声估计的宽带语音增强算法

为解决传统算法对噪声适应性较差,残留音乐噪声较强的问题,本文提出了一种基于自适应噪声估计的宽带语音增强算法。该算法可应用于宽带语音编码器,以提升在噪声环境下的编码质量。本文所提算法利用谱熵对噪声类型进行有效的判别,将背景噪声分为白噪声和有色噪声两类,并根据噪声特性选择适当的噪声估计方法。在白噪声背景下,选择一种谱平滑的方法;在有色噪声背景下,则选择经典的最小值控制递归平均算法。在此基础上结合经典的统计模型方法,构建一种具有较强噪声鲁棒性的宽带语音增强算法。在ITU-T G.160标准下对算法进行性能测试,测试结果表明,在不同强度的背景噪声环境下,增强语音的信噪比提高都较为明显。同时,在低信噪比情况下,该算法有效的抑制了严重影响听觉质量的音乐噪声现象。      

结合差分阵列与幅度谱减的双麦语音增强算法

为了抑制小型语音通信设备中的方向性噪声干扰问题,提出了一种结合差分阵列与幅度谱减的双麦语音增强算法。该算法首先利用一阶差分阵列技术,对两麦克风采集到的带噪语音信号进行处理,得到语音通道信号和噪声通道信号。接着利用差分阵列处理后的两通道信号对语音通道信号的信噪比进行估计。最后利用幅度谱减法对语音通道信号中残留噪声进行消除。针对语音通道信号的信噪比估计,本文给出了两种新奇的计算方法。仿真实验表明,该算法有效的抑制了方向噪声,改善了语音的质量,去噪效果及语音质量均优于对比算法。      

含噪语音信号频谱增强技术的统计方法研究与展望

基于单个麦克风的含噪语音信号频谱增强技术,一直受到有关工业和学术界的高度关注,其广泛应用于诸如语音识别、助听系统和免提终端通信等领域中。本文系统地讨论了含噪语音信号频谱增强系统设计的基本模块元素,并对诸如语音信号估计、语音信号出现概率估计、先验信噪比(SNR)估计和噪声功率谱估计等模块元素的统计技术与方法进行了较详细的讨论和描述。文中还讨论了含噪语音信号频谱增强算法的有关选择问题,并展望了其今后可能的研究与发展方向。     

Subband-based blind signal separation for noisy speech recognition

A method for directly extracting clean speech features from noisy speech is proposed. This process is based on independent component analysis (ICA) and a new feature analysis technique for reducing the computational complexity of the frequency domain ICA. For noisy speech signals recorded in real environments, this method yielded a considerable performance improvement     

基于分数阶谱相减的语音增强法

该文提出了基于分数阶谱相减的语音增强法(FSS)。该方法通过对带噪语音信号作分数阶傅里叶变换(FRFT),将得到的分数阶语噪混合谱与估计的分数阶噪声谱相减,最后利用分数阶Fourier反变换获得去噪后的语音信号。理论分析表明,所提方法存在一个最佳分数阶阶数,使得语噪混合信号能在分数阶变换域得到最好的分离,从而有效地提高了增强语音的性能。计算机仿真表明,对于混有加性白噪声的男/女声发音信号,所提方法在信噪比提高量和Itakura距离减少量两个方面都优于传统的谱相减法(SS),并且增强语音中的音乐噪声得到了明显抑制。     

Morphological undecimated wavelet decomposition for fault location on power transmission lines

This paper presents a novel morphological undecimated wavelet (MUDW) decomposition scheme for fault location on power transmission lines. The MUDW scheme is developed based on the morphological wavelet (MW) theory for both the extraction of transient features and noise reduction in signal processing. The analysis operators and the synthesis operator of MUDW strictly satisfy the pyramid condition. In this paper, the MUDW scheme is used to extract features from noise imposed fault-generated transient voltage and/or current signals of power transmission lines. The efficiency of the MUDW scheme used for noise reduction and the extraction of sudden changes in the transient signals are evaluated in simulation studies. The simulation results show that the fault location can be accurately detected in noisy environments.     

坦克舱强噪声背景下利用掩蔽效应的语音增强

采用了一种基于人耳听觉掩蔽效应的语音增强算法。该算法通过计算每一帧语音信号各个关键频率段的听觉掩蔽阈值,动态地调整谱减系数,有选择性地进行谱减。通过对采集的坦克舱内含强噪声的语音信号的计算机仿真表明,该算法优于基本谱减法,不仅信噪比有较大的提高而且有效地减少了主观听觉的失真和残留音乐噪声。     

基于河南方言的低信噪比下基音检测算法研究

基音检测是河南方言语音信号处理中的一个重要环节,针对低信噪比环境下的河南方言语音基音检测准确率低的问题,提出了一种语音信号增强和基音检测相结合的算法.通过多窗谱估计的改进谱减法对语音信号进行降噪处理,对增强后的语音信号用中心削波法消除偏离基音轨迹的野点,再通过自相关法实现基音检测.仿真结果表明,对于低信噪比环境下河南方言语音信号的基音估值检测结果准确,估算出的基音频率和实际基音频率能很好的重合.     

基于小波变换和独立分量分析的含噪混叠语音盲分离

含噪混叠语音的分离是语音信号处理中的重要研究问题。该文针对语音信号的非平稳特性与不同语音源之间的相互独立性,提出用小波变换与独立分量分析相结合的方法来进行分离。首先利用小波变换分别对各含噪混叠语音进行消噪,然后用独立分量分析的方法对消噪后的混叠信号进行分离,最后进一步对分离信号作矢量归一和再消噪处理,得到各个语音源信号的最终估计。仿真结果表明这种方法取得了很好的分离效果。     

基于最小统计量和掩蔽效应的单通道语音增强

利用人耳感知的掩蔽特性，并结合含噪语音能量的最小统计量估计，提出了一种低信噪比下的单通道语音增强算法。该算法对原始语音在Bark频带能量的最小统计量进行估计，从而准确估计含噪语音信噪比，再从感知的角度，在时域和Bark频域上合理调整增强系数，以实现语音增强的目的。实验表明，该增强算法能够在减小语音失真的同时，很好地抑制背景噪声和残余音乐噪声。     

基于小波变换和盲信号分离的多通道肌电信号处理方法

 为了消除多通道表面肌电信号(SEMG)采集时形成的混叠现象,提出一种新的SEMG处理方法.该方法将小波变换和独立分量分析(ICA)结合,利用小波变换的去噪作用,滤除混合在原始SEMG中的部分噪声后作为ICA的输入信号,采用Infomax算法对输入信号实施盲分离,并引入相关系数验证ICA分量与源信号的一致性.实验结果表明,该方法用于多通道SEMG的盲分离是很有效的.