Bark子带小波包自适应阈值语音去噪方法

为了克服低信噪比输入下,语音增强造成清音弱分量损失,导致信号重构失真的问题,提出了一种新的语音增强方法。该方法采用小波包拟合语音感知模型的临界带,按子带能量对语音清浊音分离,然后对清音和浊音信号分别作8层和4层小波包分解,在阈值计算上采用Bark子带小波包自适应节点阈值算法,在Bark子带实时跟踪噪声水平,有效保护清音中高频弱分量,减少失真。通过与传统语音增强方法的仿真对比实验,证实该方法在低信噪比输入时,具有明显优势,输出信噪比高,语音失真度低。将该方法与谱减法相结合,进行语音二次增强,能进一步提高增强语音质量。     

一种基于清浊音分离的动态阈值小波去噪方法

低信噪比下,传统的小波去噪算法会造成语音信号中有用信息的损失,从而导致去噪性能的下降。针对这一问题,提出了一种基于清浊音分离的动态阈值小波去噪方法。采用谱减法去除部分噪声,再运用短时能量法判别清浊音,有效地降低了误判率;融入了小波包分解法以保护清音部分不被损失;根据各层的分解系数来动态地确定阈值,以避免过平滑真实信号;采用了一种新的阈值函数,有效弥补了软、硬阈值函数在去噪性能上的不足。仿真结果表明,该方法能较好地提高语音信号的重构质量。     

基于改进小波阈值-CEEMDAN算法的ECG信号去噪研究

Electrocardiogram (ECG) signal denoising has always been a hot research issue. In order to eliminate the noises in ECG signal, a denoising method based on adaptive complete set empirical mode decomposition (CEEMDAN) and wavelet improved threshold function is proposed. Firstly, this method firstly decomposes the ECG signal by CEEMDAN to obtain a set of intrinsic modal functions (IMFs) from high frequency to low frequency. CEEMDAN decomposition is performed on ECG signal to yield several modal components (IMF). Secondly, the correlation coefficient method is used to perform wavelet denoising with improved threshold on the high frequency IMFs. For the low frequency IMFs, by setting a fixed threshold, the IMFs below the threshold is considered to be the baseline drift signal and removed. Finally, the denoised IMFs and the retained IMFs are reconstructed. The experimental results show that the proposed method is more effective than the empirical mode decomposition (EMD) wavelet denoising, and the global average empirical mode decomposition (EEMD) wavelet denoising method.     

语音清浊音分类及浊音谐波提取算法——三阶累积量基于正弦语音模型的应用

在低信噪比和非平稳噪声干扰下,语音信号的清浊音检测是语音信号处理中的一个重要研究问题。论文基于语音正弦模型,提出了一种清浊音分类和浊音谐波提取算法。该方法在分析了语音的三阶累积量谱后,用子谐波-谐波方法取得基音,并计算出谐波参数和高低频能量比值。它利用谱包络估计器得到谱包络及尖峰信号,结合最小均方估计准则下的迭代算法计算语音谐波的信噪比;通过对上面各计算结果的综合评价得出语音帧的浊音度,从而得到语音清浊音的分类和浊音谐波数。仿真结果表明,该算法在复杂噪声背景下,能有效进行语音分类,准确得到浊音度。同时该算法还具有实时性好、语音参数分析精度高的特点。     

基于改进的小波阈值函数语音增强方法

传统的小波阈值去噪方法会造成有用语音信号的损失, 信噪比改善情况不理想. 通过分析小波去噪原理, 提出了一种改进的小波阈值函数语音增强方法. 该方法结合小波软、硬阈值函数去噪的优点, 克服了硬阈值函数的不连续及软阈值函数存在偏差的缺点. 该方法首先对清浊音信号进行判断, 接着采用变化的阈值对清浊音信号的小波系数进行不同的阈值处理. 仿真实验结果表明, 改进的方法非常适用于强噪声背景下的语音增强, 无论在保留含噪语音信号中的清音信息, 还是在信噪比改善指标上均优于传统的软阈值法、谱减法和听觉感知小波变换法.     

改进EEMD算法在心电信号去噪中的应用

集合经验模态分解（Ensemble empirical mode decomposition,EEMD）方法在去除心电信号噪声时,噪声本征模态函数（Intrinsic mode function,IMF）分量难以选择且将噪声分量直接去掉会导致信号失真。针对上述问题,提出了一种基于EEMD的自适应阈值算法。首先对含噪心电图（Electrocardiogram,ECG）数据进行EEMD分解,得到IMF,根据马氏距离进行信号IMF分量和噪声IMF分量的判定,然后通过果蝇优化算法确定噪声IMF的阈值,将经过阈值去噪的新的分量和剩余分量重构得到去噪后的ECG。最后,使用MIT-BIH数据库中的心电数据进行实验,实验结果表明,该方法在去噪同时能够较好地保留信号细节。     

基于清浊音分离的优化小波阈值去噪方法

结合小波阈值去噪和清浊音分离技术,提出了一种优化的语音去噪新方法。首先,针对语音清音部分往往包含有许多类似噪声的高频成分的特点,对其直接进行小波阈值去噪很可能误除了这些高频成分,造成失真,因此有必要先对语音进行清浊音分离。其次,通过对不同小波函数、阈值选取规则以及阈值处理函数的优化,选择最佳的小波去噪方法。仿真结果表明,与经典小波阈值去噪方法相比,提出的方法既尽可能地去除噪声,又保留了原来语音的特征,较大地提高了语音质量。     

Effectiveness of Teager energy operator for epoch detection from speech signals

In this paper, we try to present the problem of epoch detection from a different perspective that not only deals with estimation of epoch instances (i.e., glottal activity) but also with quantification of the absence of epochs (i.e., no glottal activity) in the unvoiced regions of speech signal. Most of the epoch detection methods perform significantly well in the voiced regions of speech but are not robust enough in the unvoiced regions of speech, i.e., they detect a number of pseudo epochs in the unvoiced regions of speech. We propose a simple method based on Teager Energy Operator (TEO) which not only determines the epochs in voiced region (due to its superior temporal resolution and its ability to capture airflow properties through the glottis) but also is very effective in unvoiced region. Recently proposed methods such as 0-Hz resonator-based method and DYPSA method gave a combined rate (CR) (for detecting epochs in voiced and unvoiced regions of speech) of 74.7% and 60%, respectively and a pseudo epoch rate (PER) (i.e., spurious epochs in the unvoiced regions of speech) of 62.9% and 54.04%, respectively. On the other hand, our proposed method gave a CR and PER of 87% and 0.27%, respectively. This result suggests that the proposed method captures glottal activity more efficiently both in voiced and unvoiced regions of speech signal. The performance of the proposed method is demonstrated using publicly available CMU-Arctic database using the epoch information from the electro-glottograph (EGG) as reference signal to serve as ground truth for estimation of glottal closure instants (GCI). Due to the noise suppression capability of TEO, the proposed method has almost no or little effect (i.e., robust) against signal degradations like white, babble, high frequency and vehicle noises as compared to 0-Hz resonator and DYPSA methods.     

基于夹角余弦和模糊阈值的EEMD去噪方法

针对非线性非平稳信号的去噪问题,结合EEMD分解信号的自适应特性,提出一种基于夹角余弦和模糊阈值的去噪方法。首先用夹角余弦法计算各个本征模态函数（IMF）与观测信号之间的相似度,以相似度曲线的首个极小值的后一个位置为分界点将分解出的IMF分为噪声主导模态和信号主导模态;然后根据VisuShrink阈值易“过扼杀”细节系数和SUREShrink阈值易“过保留”噪声系数的特点,利用模糊阈值对噪声主导的IMF进行处理;最后将所有的IMF重构得到消噪信号。分别采用仿真信号和真实ECG信号进行去噪实验。结果表明,所提方法在整体性能上优于小波半软阈值方法、基于EMD的软阈值（EMD-Soft）和间隔阈值（EMD-IT）方法,是一种有效的去噪方法。     

Artificial neural network based autoregressive modeling technique with application in voice activity detection

A new method of estimating the coefficients of an autoregressive (AR) model using real-valued neural network (RVNN) technique is presented in this paper. The coefficients of the AR model are obtained from the synaptic weights and adaptive coefficients of the activation function of a two layer RVNN while the number of neurons in the hidden layer is estimated from over-constrained system of equations.The performance of the proposed technique has been evaluated using sinusoidal data and recorded speech so as to examine the spectral resolution and line splitting as well as its ability to detect voiced and unvoiced data section from a recorded speech. Results obtained show that the method can accurately resolve closely related frequencies without experiencing spectral line splitting as well as identify the voice and unvoiced segments in a recorded speech.     

基于胞腔均匀度的清浊模式码书设计算法

清音和浊音线谱频率（LSF）参数分布具有差异性。为了提高声码器中LSF参数的量化性能,利用胞腔均匀度（CE）能定量表征清浊音LSF参数分布的差异程度,提出了一种基于CE的清浊模式码书设计算法。该算法首先根据CE推导出清音和浊音参与训练的LSF参数的数量比;然后剔除清音中指定数量的非典型LSF参数;最后重新训练出码书。实验结果表明,在相同码率情况下,该算法较码书共享算法谱失真降低2.5%,平均意见得分提高了2.3%,码书存储量下降了21.1%,并且适用于不传输清浊音标志的声码器。     

一种高精度的基音提取方案

为了得到准确的基音曲线,该文提出了一个有效的解决方案,通过利用人工神经网络进行浊音判决,以及计算基音频率时利用基于 自相关函数的动态规划算法,有效地克服了在语音信号清音和无声段错误的基音提取以及在浊音段的2倍频或1／2倍频错判。实验证明,利用 该文提出的方案,可以大大提高基音频率提取的准确度,从而得到非常准确的语音基频曲线。     

语音通信中信号的清浊音判别

文章采用了一种基于三参数组合的方法对语音信号进行清／浊音判决。在传统两参数（短时能量和过零率）算法的基础上增加了自相关函数作为判决参数,减少了清／浊音信号的误判率。经计算机仿真表明,算法切实可行,且算法误判概率小。     

基于奇异谱分析的经验模态分解去噪方法

提出了一种基于奇异谱分析(SSA)的经验模态分解(EMD)去噪方法。该方法先对带噪信号进行EMD分解,得到若干个本征模态函数(IMF)。再通过SSA对每个IMF分量进行去噪处理:把第一个IMF分量作为高频噪声,并根据它计算出剩余IMF中所含的噪声能量,从而得到剩下的每个IMF中信号所占的能量比值。然后选择合适的窗口长度,对每个IMF进行SSA变换,根据IMF中信号所占的能量比值选择合适的奇异值分解(SVD)分量重构,得到去噪后的IMF。再将所有重构得到的IMF分量以及余项相加,得到最终去噪后的信号。经过实验,对比研究了该方法与小波软阈值、EMD软阈值和EMD滤波方法的去噪效果,结果表明该方法整体优于其它方法,是一种有效的信号去噪方法。     

结合听觉掩蔽效应的时频自适应小波阈值增强

针对浊音、清音和噪声的不同特性,结合听觉掩蔽并使用随尺度变化的多阈值对语音信号进行处理.提出了多小波门限估计法,该方法针对不同声音成分,使用不同的与尺度有关的缩小因子调节门限值;通过估计各频带内的信噪比,实现了阈值的时频自适应变化;用巴克小波包分解法模拟人耳临界带特性,用小波谱减法对带噪语音进行预增强,采用Johnst...     

基于EMD阈值算法的脉冲涡流信号消噪

针对脉冲涡流信号夹杂着较多的高频噪声,提出了一种新的经验模态分解阈值消噪算法。首先将信号分解为多个本征模态函数（Intrinsic Mode Function,IMF）,对信噪比低的高频IMF进行减小噪声能量处理后得到重组信号;再对重组信号进行EMD分解后根据白噪声统计特性对IMF筛选,对噪声含量多的IMF进行小波阈值消噪;最后将处理过的IMF与噪声含量少的IMF重构得到消噪后的信号。实验仿真的结果和数据表明,该方法可以减少失真,获得更高的信噪比,能够较好地消除噪声的干扰恢复出原始的信号。     

分形理论和神经网络在清浊音判决中的应用

采用混沌信号处理方法中的分形理论对信号进行分析。分形维数很好的体现了信号的混沌程度,而清音和浊音由于在发声原理上的不同,清音类似于噪声,浊音具有近似的周期性,在分形维这个特征上体现出差异。首先对语音信号分帧求分形维轨迹,计算出平均分形维,然后在分形维参数的基础上提出DP值特征参数,以分形维与DP值作为一个特征向量,采用BP神经网络进行识别,得到了很好的识别效果。     

A novel sub-band adaptive filtering for acoustic echo cancellation based on empirical mode decomposition algorithm

Acoustic echo cancellation is one of the most severe requirements in hands-free telephone and teleconference communication. This paper proposes an Empirical Mode Decomposition (EMD)-based sub-band adaptive filtering structure, which applies the EMD-based algorithm dealing with the far-end speech signal and the microphone output to obtain two sets of intrinsic mode functions (IMFs). In addition, each IMF set is separated into different bands based on the power spectral density (PSD) of every IMF. Experiment signals were collected from a medium-size office room and simulations were taken under different conditions by three types of EMD-based algorithms. Results show that the proposed structure is able to model the transfer function of the unknown environment and track the change of the room much faster than the normalized adaptive filtering structure. The ensemble EMD (EEMD) algorithm and the noise-modulated EMD (NEMD) are proved to have better performance than the EMD algorithm in terms of echo return loss enhancement.     

Parametric modeling of speech by complex AM and FM signals

Complex AM and FM signal models can be used for parametric modeling of speech signals. Complex AM signal model has been found to be suitable for voiced speech phonemes, whereas complex FM signal model can be used for representation of unvoiced speech phonemes. This article explains the basic principles of parameter estimation of these two models, and presents techniques for fast on-line processing of speech data and automated model fitting.     

A better decomposition of speech obtained using modified Empirical Mode Decomposition

The objective of this work is to obtain meaningful time domain components, or Intrinsic Mode Functions (IMFs), of the speech signal, using Empirical Mode Decomposition (EMD), with reduced mode mixing, and in a time-efficient manner. This work focuses on two aspects – firstly, extracting IMFs of the speech signal which can better reflect its higher frequency spectrum; and secondly, to get a better representation and distribution of the vocal tract resonances of the speech signal in its IMFs, compared to that obtained from standard EMD. To this effect, modifications are proposed to the EMD algorithm for processing speech signals, based on the critical nature of the interpolation points (IPs) used for cubic spline interpolation in EMD. The effect of using different sets of IPs, other than the extrema of the residue – as used in standard EMD – is analyzed. It is found that having more IPs is beneficial only upto a certain limit, after which the characteristic dyadic filterbank nature of EMD breaks down. For certain sets of IPs, these modified EMD processes perform better than EMD, giving better frequency separability between the IMFs, and an enhanced representation of the higher frequency content of the signal. A detailed study of the distribution of the formants, in the IMFs of the speech signal, is done using Linear Prediction (LP) analysis of the IMFs. It is found that the IMFs of the EMD variants have a far better distribution of the formants structure within them, with reduced overlapping amongst their filter spectrums, compared to that of standard EMD. Henceforth, when subjected to the task of formants estimation of voiced speech, using LP analysis, the IMFs of the modified EMD processes cumulatively exhibit a superior performance than that of standard EMD, or the speech signal itself, under both clean and noisy conditions.