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。     

EEMD在土壤剖面反射光谱消噪中的应用

实测光谱常含有大量干扰信息,消噪在光谱数据处理和分析中极为重要,它直接影响后续的定量分析和信息挖掘。因此,选择适当的消噪方法是改善光谱分析精度,提升光谱分析能力的一个关键性突破。集合经验模态分解(EEMD)方法是一个以信号固有特征尺度为度量的时空滤波过程,能充分保留信号本身的非线性和非平稳特征,在信号的滤波和消噪中具有较大的优势。结合EEMD的多尺度滤波特性,提出了一种新的EEMD阈值光谱消噪方法,并应用于新疆塔里木河中游典型绿洲33个土壤剖面反射光谱数据的预处理。为探讨EEMD阈值法在土壤剖面反射光谱消噪中的效用,对EEMD阈值法和小波阈值法的消噪结果进行了对比分析。结果表明：与传统的小波阈值法相比,EEMD阈值法消噪结果的信噪比从14.836 6 dB提高到34.275 7 dB,均方根误差由6.786 1×10-5降到7.240 6×10-6,相关系数从0.982 5提高到0.999 8,EEMD阈值法的三个消噪效果衡量指标均优于小波阈值法。证明了EEMD阈值法可有效地去除土壤剖面光谱噪声,较好地保留了光谱的细节信息,提高了光谱的定量分析精度,且与小波阈值消噪方法相比具有较强的可靠性和自适应优势,作为光谱数据预处理的一种新方法,其应用前景良好。     

激光主动探测中回波信号的小波去噪方法

在分析激光主动探测中回波信号的噪声特性和小波变换去噪原理的基础上，提出了一种基于最大信噪比准则的小渡阈值去噪方法。首先用最大信噪比准则对小波变换系数进行阈值选取，然后采用软阂值方法对小波系数进行量化处理后再重构。仿真结果表明最大信噪比准则小波去噪方法改善信噪比效果十分显著，检测下限达到-16．2dB。证明了该方法在激光主动探测系统回波信号检测中的有效性。     

Noise reduction for lidar returns using self-adaptive wavelet neural network

Lidar has been widely applied in many fields, such as meteorology and environment. However, because lidar returns are very weak, the influence of noise on useful signal is very serious. To obtain useful lidar return signals from raw data, a self-adaptive method combining wavelet analysis and a neural network that suppresses noise is proposed, in which the orthogonal Daubechies wavelet family serves as node functions in the hidden layer of the neural network, a search algorithm is selected to optimize the parameters and thresholds, and the Levenberg–Marquardt algorithm is adopted in the neural network gradient algorithm. Some comparative experiments were carried out to verify the feasibility of the noise reduction method and the results showed that the signal-to-noise ratio (SNR) of the common wavelet threshold denoising method is about 10, while that of the self-adaptive wavelet neural network denoising method is more than 20. From the experimental results, it can be seen that the wavelet neural network denoising method has less distortion and a higher SNR value than other methods, giving it superior performance.     

Noise reduction for lidar returns using local threshold wavelet analysis

Remote sensing technique of lidar belongs to the category of weak signal extraction under strong background noise. For effectively reducing the noise of lidar return signal, a wavelet analysis method using local threshold value is employed. In the local threshold value wavelet method, different threshold values are used to quantify the high frequency coefficients of every decomposition layer. Both the numerical simulation signal contaminated by random noise of different standard deviation and the practical Mie lidar returns were adopted, and the comparisons among sliding-window method, global threshold method and local threshold method were performed for verifying the feasibility of the local threshold method. Experiment results show that the local threshold wavelet method is a useful de-noising method which shows better effects of noise reduction than other two methods.     

Extraction of weak crack signals based on sparse code shrinkage combined with wavelet packet filtering

Early crack signals in critical infrastructure components of major equipment are hardly to be extracted due to its low signal noise ratio (SNR). A de-noising method combined wavelet packet (WP) technology with sparse code shrinkage (SCS) is proposed in this study. Firstly, WP reconstruction technology is used to reserve the crack signal with a specified frequency range. That is, the signal is decomposed by Meyer wavelet into five layers, and the signal with the frequency range from 187.5 kHz to 609.375 kHz is reserved. Then SCS method removes noise within the specified frequency range. Namely, the probability density function (PDF) of the signal independent coefficients is estimated via the generalized Gaussian model (GGM) in the independent component analysis (ICA) space. The nonlinear de-noising is finished by utilizing maximum a posteriori (MAP) estimate. The results obtained by the combined method are compared with those generated by the SCS method and the WP de-noising method. It demonstrates that the combined method is the best one among the three methods in extracting weak signals. Its output SNR is −2.38 dB and the correlation coefficient (CC) is 0.54 when the input SNR is −20 dB. They are higher than those obtained by the SCS method (SNR −4.46 dB and CC 0.51). The WP method is the worst (SNR −3.54 dB and CC −0.003). Therefore, the combined method is quite suitable for weak signal extraction.     

Research on FBG sensor signal wavelength demodulation based on improved wavelet transform

A new Fiber Bragg Grating (FBG) wavelength demodulation scheme is studied in the paper, which consists of an improved de-noising method and Gaussian fitting peak searching algorithm. The improved translational invariant wavelet without threshold adjust factor is proposed to get a better de-noising performance for FBG sensor signal and overcome the drawbacks of soft or hard threshold wavelets. In order to get a high wavelength demodulation precision of FBG sensor signal, this de-noising method is designed to combine with Gaussian fitting peak searching algorithm. The simulation results show that the wavelength maximum measurement error is lower than 1 pm, and can get a much higher accuracy.     

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

Magnetic resonance (MR) images acquired with fast measurement often display poor signal-to-noise ratio (SNR) and contrast. With the advent of high temporal resolution imaging, there is a growing need to remove these noise artifacts. The noise in magnitude MR images is signal-dependent (Rician), whereas most de-noising algorithms assume additive Gaussian (white) noise. However, the Rician distribution only looks Gaussian at high SNR. Some recent work by Nowak employs a wavelet-based method for de-noising the square magnitude images, and explicitly takes into account the Rician nature of the noise distribution. In this article, we apply a wavelet de-noising algorithm directly to the complex image obtained as the Fourier transform of the raw k-space two-channel (real and imaginary) data. By retaining the complex image, we are able to de-noise not only magnitude images but also phase images. A multiscale (complex) wavelet-domain Wiener-type filter is derived. The algorithm preserves edges better when the Haar wavelet rather than smoother wavelets, such as those of Daubechies, are used. The algorithm was tested on a simulated image to which various levels of noise were added, on several EPI image sequences, each of different SNR, and on a pair of low SNR MR micro-images acquired using gradient echo and spin echo sequences. For the simulated data, the original image could be well recovered even for high values of noise (SNR approximately 0 dB), suggesting that the present algorithm may provide better recovery of the contrast than Nowak's method. The mean-square error, bias, and variance are computed for the simulated images. Over a range of amounts of added noise, the present method is shown to give smaller bias than when using a soft threshold, and smaller variance than a hard threshold; in general, it provides a better bias-variance balance than either hard or soft threshold methods. For the EPI (MR) images, contrast improvements of up to 8% (for SNR = 33 dB) were found. In general, the improvement in contrast was greater the lower the original SNR, for example, up to 50% contrast improvement for SNR of about 20 dB in micro-imaging. Applications of the algorithm to the segmentation of medical images, to micro-imaging and angiography (where the correct preservation of phase is important for flow encoding to be possible), as well as to de-noising time series of functional MR images, are discussed.     

一种改进的MEMS陀螺信号去噪方法研究

 从工程实用的角度出发,探讨了MEMS陀螺仪随机漂移误差的有效补偿方法。根据小波阈值去噪原理,结合多项式函数插值法提出了一种MEMS陀螺仪输出信号的有效去噪补偿方法,克服了传统软、硬阈值去噪方法的缺陷,通过对MEMS陀螺数据分析研究,验证了该方法对于MEMS陀螺输出信号滤波消噪的优越性。     

EEMD自适应去噪在拉曼光谱中的应用

二代小波是公认较好的降噪手段,但是降噪效果依赖于基函数、分解层数和阈值等参数设置。经验模态分解(empirical mode decomposition, EMD)无需参数设定,按照频率特性将信号分解成本征模函数(intrinsic mode function, IMF),对IMF滤波,实现了信号自适应去噪。拉曼光谱中信号和噪声交叠集中在极高频段,EMD产生模态混叠问题,影响去噪效果。应用总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)拉曼光谱克服了模态混叠,有效区分出高频信号和噪声,获得了与小波函数相似去噪效果。文中首先对一段非线性非平稳豆油脂拉曼光谱EMD分解,可见模态混叠,EEMD分解出清晰模态的特征分量。然后分别用快速傅里叶变换(fast Fourier transform,FFT)、小波变换(Wavelet)、EMD和EEMD处理含噪光谱,信噪比、均方根误差、相关系数三个方面指标表明FFT高频去噪效果最差,其次是EMD,恰当的Wavelet同EEMD效果相当,EEMD的优势是降噪过程的自适应。最后提出光谱时频分析方法和IMF噪声属性判别准则研究趋势。     

Treatment of MZI sensor signal with an improved wavelet neighboring coefficients

In order to gain the wavelength drift of Mach–Zehnder interferometer (MZI) sensor system, achieve the measurement of ambient environment parameters precisely, so, need to treatment optical fiber sensing signal with noise. Firstly, introduce the method of neighborhood wavelet coefficient by analyzing the drawback of traditional wavelet de-noising methods. Secondly, an improved threshold is putted forward based on neighboring coefficients in this paper which is to overcome the shortcomings of traditional wavelet methods. Finally, the threshold function proposed by this paper deal with MZI sensing noisy signal together with improved threshold. The simulation result shows that the new method can get the better signal-to-noise ratios (SNR) and root of mean square error (MSE) simultaneously and gain the reconstruction signal of the higher correlation coefficient (CC). Compared with the soft and hard threshold method, its SNR increases by 2–4 db, and compared with NeighCoeff, its SNR improves by 1.2 db. The peak error is 1.6 pm. So, the system can meet the requirement of improving sensor detection precision.     

采用双方波信号和B-样条小波解调弱反射光纤布拉格光栅

提出了一种采用双方波信号和B-样条小波解调弱反射光纤布拉格光栅(WFBG)的方法,并进行了实验验证.单个方波周期设置为相邻WFBG间光纤中激光往返传输的时间,对单个方波进行猝发操作形成双方波,则前WFBG反射的后方波与后WFBG反射的前方波重叠干涉.采用B-样条小波变换降低干涉信号的噪声,利用Hilbert变换对干涉信号进行π/2相移,对原干涉信号和相移后干涉信号比值进行反正切运算,得到干涉信号的相位信息.将间隔为50m的5-WFBG阵列置于木地板上,分别接收不同振幅和频率的正弦声波,采用上述方法解调的干涉相位信号能较好地反映声波信息.该解调方法解调光路简单,数据处理简单.     

Signal-to-noise ratio enhancement based on wavelet filtering in ultrasonic testing

In ultrasonic non-destructive testing of materials with a coarse-grained structure the scattering from the grains causes backscattering noise, which masks flaw echoes in the measured signal. Several filtering methods have been proposed for improving the signal-to-noise ratio. In this paper we present a comparative study of methods based on the wavelet transform. Experiments with stationary, discrete and wavelet packet de-noising are evaluated by means of signal-to-noise ratio enhancement. Measured and simulated ultrasonic signals are used to verify the proposed de-noising methods. For comparison, we use signal-to-noise ratio enhancement related to fault echo amplitudes and filtering efficiency specific for ultrasonic signals. The best results in our setup were achieved with the wavelet packet de-noising method.     

光谱信号的小波去噪新技术

光谱分析中,噪声的存在常影响分析的准确度和检测限。现有滤波方法在光谱信号除噪方面有种种缺陷。文章充分利用小波在信号处理方面的优良特性,提出了Mexican Hat小波滤波算法,它选用Mexican Hat小波函数构造滤波项,利用滤波项与原始信号作用,从而实现信号与噪音的分离。该方法无论对低频或高频信号均适用,除噪完全,即使对信噪比为1的高噪声信号也能获取满意的处理结果。运用这一技术处理光谱信号,简单快速、结果可靠,处理后峰位置、峰高、峰面积误差分别小于0.2%,3.2%,1.1%。大量实验表明,本方法能有效提高光谱分析的准确度。     

Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics

De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method.     

小目标识别的小波阈值去噪方法

为改善小目标识别的滤噪效果并提高其信噪比,构造了新的阈值函数并采用局部方差估计法来计算阈值对小目标进行去噪处理。对小波分解层次中各高频子带选取不同的阈值,其中大于阈值的小波系数采用改进的双曲线函数作为阈值函数,小于阈值的小波系数采用指数函数与对数函数相互组合的方式作为阈值函数。对采用的阈值函数进行了理论推导,并与软、硬阈值法进行了实验对比。计算机仿真结果表明：经本文阈值法处理后,信噪比相对于含噪图像提高了70.8%,而软、硬阈值法分别提高了49.8%和59.7%。光学实验进一步证实：该方法能更有效地提高信噪比,增强联合变换相关器对于小目标的识别能力。     

A LLS operator based S-I WT de-noising algorithm applied in EDXRF

An improved shift-invariant wavelet (S-I WT) de-noising algorithm based on LLS operator is proposed for high-resolution energy dispersive X-ray fluorescence. Sym8 is chosen as the wavelet basis function and performed noise reduction on the analog signal. Comparison of the de-noising effect of S-I WT, improved WT and LLS S-I WT (where LLS is the log square root operator) method are quantitatively evaluated by using evaluation criteria signal-to-noise-ratio (SNR), root mean square error and Pearson correlation coefficient. Meanwhile, a new evaluation criterion of de-noising effect, called peak area relative difference, is also proposed to evaluate the counting deviation. The results show that the LLS-SI WT is simple and reliable, can effectively reduce pseudo-Gibbs artificial signals and statistical fluctuation. Besides, this method simplifies the calculation, reduces the running time and improves the running efficiency. The LLS-SI WT is also applied to reduce the noise after adding strong noise to the signal, the SNR has been improved from 14.0040 to 14.7552, and most of the characteristic peak information retains to the greatest extent.     

基于小波变换的体内外酒精含量近红外光谱检测与分析

应用小波分析对体外和体内的酒精近红外光谱信号进行去噪分析,通过体外光谱分析确定酒精吸收峰特征范围,为体内近红外光谱分析确定有效区间。软阈值和硬阈值下,分别采用缺省阈值、Birge-Massart阈值和最大最小值阈值,比较酒精光谱去噪,信噪比(signal noise ratio,SNR)和均方根误差(root mean square error,RMSE)去噪效果。结果表明:缺省硬阈值方法对酒精近红外光谱去噪的效果较好;小波变换可以有效去除酒精近红外光谱的噪声,提高信噪比,保留有用真实信号。在不同的酒精浓度下,去噪后的近红外光谱能够较好的显示浓度变化规律。小波分析在近红外光谱法对人体酒精无创检测及定量分析方面有较好的应用前景。     

Lidar signal de-noising based on wavelet trimmed thresholding technique

Lidar is an efficient tool for remote monitoring, but the effective range is often limited by signal-to-noise ratio (SNR). By the power spectral estimation, we find that digital filters are not fit for processing lidar signals buried in noise. In this paper, we present a new method of the lidar signal acquisition based on the wavelet trimmed thresholding technique to increase the effective range of lidar measurements. The performance of our method is investigated by detecting the real signals in noise. The experiment results show that our approach is superior to the traditional methods such as Butterworth filter.     

A closer look into DESIRE for NMR microscopy

The major challenge of nuclear magnetic resonance (NMR) microscopy at a spatial resolution of a few micrometers is to obtain a sufficiently high signal-to-noise-ratio (SNR) within a reasonable measurement time. As a particular difficulty, molecular self-diffusion poses a serious limitation to true spatial resolution and SNR if conventional Fourier encoding techniques are used. Opposed to that, the alternative DESIRE (Diffusion Enhancement of SIgnal and REsolution) approach to NMR microscopy utilises diffusion to increase the SNR. Being a real-space imaging method, spatial localisation is accomplished by saturation pulses while diffusion continuously replaces the saturated by unsaturated spins. For this technique a signal enhancement of up to three orders of magnitude has been predicted and initial experimental data have provided a proof of principle. In the present work, a detailed investigation of one-dimensional (1D) DESIRE is presented including simulations of a real implementation of the method, a quantitative experimental analysis, and basic 1D imaging. The simulations reveal the importance and provide the means of ensuring the true spatial resolution for this particular way of localisation, enable the selection of useful experimental parameters, and predict the specific image contrast to be expected around barriers restricting diffusion. Experimental data are presented with resolutions down to 3 microm and DESIRE enhancement up to 25 that are in good agreement with the simulation results. In particular, 1D DESIRE imaging in a phantom confirms the expected signal drop close to barriers due to spatially restricted diffusion.