Time-frequency Wiener filtering for structural noise reduction

In order to enhance the defect in relation to background noise of large grained materials different algorithms have been developed. Wiener filtering techniques have proved to be efficient for the SNR enhancement of ultrasonic signals coming from highly scattering materials. These processing algorithms are based on designing a filter that has large gain at frequencies where the SNR is high and low gain at frequencies where SNR is small. However, this technique does not consider two important ultrasonic effects: the finite-time duration of the flaw UT signal coming from a defect and the distortion of the frequency components of the traveling wave-front due to the dispersion. In this work, a time-frequency Wiener filter is proposed that takes into account these two characteristics. Experimental results are presented, showing that the proposed time-frequency algorithm has an excellent performance on SNR enhancement.     

Wavelet based noise suppression technique and its application to ultrasonic flaw detection

The noise suppression techniques with wavelet transform (WT) are widely used in non-destructive testing and evaluation (NDT&E), especially in ultrasonics. Complete reconstruction theory with hard or soft thresholds, reconstruction technique based on the singularities of noise and signal, matched filter with an impulse response, and optimal frequency-to-bandwidth ratio of wavelet technique have all been used to analyze ultrasonic signals for noise suppression. But a more simple and effective technique has been pursued for decades. This paper develops a new technique using WT for the right purpose. In this work, WT is treated as a band-pass filter whose central frequency and frequency bandwidth (CF&FB) are determined by the spectra distribution of an ultrasonic signal captured from real testing situation. For the purpose of matching their CF&FB well, a technique for evaluating the optimal scale of a daughter wavelet is carried out too. By acting this daughter wavelet as a band-pass filter, we can obtain excellent de-noising results, even when the signal to noise ratio (SNR) is below -18 dB. The performance of the technique has been done by ultrasonic signals with computer generated white noises. Finally, the experimental verification is performed on a pipeline specimen with man-made small flaws with good results obtained. The results show that the technique is more suitable for processing heavy noised ultrasonic signals, and it can also be used in automatic flaw detection.     

混沌信号自适应协同滤波去噪

混沌信号协同滤波去噪算法充分利用了混沌信号的自相似结构特征,具有良好的信噪比提升性能.针对该算法的滤波参数优化问题,考虑到最优滤波参数的选取受到信号特征、采样频率和噪声水平的影响,为提高该算法的自适应性使其更符合实际应用需求,基于排列熵提出一种滤波参数自动优化准则.依据不同噪声水平的混沌信号排列熵的不同,首先选取不同滤波参数对含噪混沌信号进行去噪,然后计算各滤波参数对应重构信号的排列熵,最后通过比较各重构信号的排列熵,选取排列熵最小的重构信号对应的滤波参数为最优滤波参数,实现滤波参数的优化.分析了不同信号特征、采样频率和噪声水平情况下滤波参数的选取规律.仿真结果表明,该参数优化准则能在不同条件下对滤波参数进行有效的自动最优化,提高了混沌信号协同滤波去噪算法的自适应性.     

Signal-to-noise ratio enhancement based on the whitening transformation of colored structural noise

In the ultrasonic testing and evaluation of highly scattering materials (i.e. non-homogeneous media such as composites, layered and clad materials) structural noise is an important limitation to the visibility of flaw echoes. This noise cannot be reduced by conventional linear filtering or by time-averaging techniques. In order to enhance the defect-to-background noise ratio (SNR), many different algorithms have been developed over the years. This work analyzes three new strategies for SNR enhancement based on the whitening transformation of the colored structural noise. By using this transformation, the small spectral differences between noise and flaw echoes are exploited, thereby allowing an improvement in the visibility of the flaw.     

A speech enhancement method based on Kalman filtering

I.IntroductionKa1manfilteringisjustamethodtoestimatestatistica1lythestateoftheobservedsystemfromthecorruptedsigna1s,andthiskindofcstimationisarecurrcneeestimationbasedon1inear,nonbiasandminimumvariance.Moreover,Ka1manfilteringisapplicabletonon-sta-honarysignalsandtime-variantdynamicsystem.Therefore,Kalmanfilteringisveryapplica-bletoenhancingthespeechsigna1sthatarecorruptedbynoise.ThispaperreportStheconcretcmethodofenhanccmentofnoisyspccchanditscxperimentresults.Experimentsindicate:Afterthes…     

Extending the articulation index to account for non-linear distortions introduced by noise-suppression algorithms

The conventional articulation index (AI) measure cannot be applied in situations where non-linear operations are involved and additive noise is present. This is because the definitions of the target and masker signals become vague following non-linear processing, as both the target and masker signals are affected. The aim of the present work is to modify the basic form of the AI measure to account for non-linear processing. This was done using a new definition of the output or effective SNR obtained following non-linear processing. The proposed output SNR definition for a specific band was designed to handle cases where the non-linear processing affects predominantly the target signal rather than the masker signal. The proposed measure also takes into consideration the fact that the input SNR in a specific band cannot be improved following any form of non-linear processing. Overall, the proposed measure quantifies the proportion of input band SNR preserved or transmitted in each band after non-linear processing. High correlation (r?=?0.9) was obtained with the proposed measure when evaluated with intelligibility scores obtained by normal-hearing listeners in 72 noisy conditions involving noise-suppressed speech corrupted in four different real-world maskers.     

DNA测序信号去噪分析的一种新方法

在DNA荧光测序中,噪声会影响分析的准确度和检出限。相比其他滤波方法,小波分析具有良好的时频域分辨特性。在小波去噪处理中,正确选择合适的小波基函数、去噪阈值和分解层数直接关系到信号去噪处理的质量。为了真实构建噪声模型并准确评价去噪算法的有效性,实验中通过实际系统中采集到的噪声信号叠加理想荧光信号构建DNA测序仿真信号,去噪分析的结果表明：选择sym7小波基函数、分解层数(lev＝5)与使用固定格式软阈值,有效去除了DNA测序信号的噪声;处理后,信号的信噪比提高了5倍以上。将其用于处理实际的DNA电泳荧光信号,相比基于随机噪声模型的算法,去噪后的信号更加真实可靠。     

Improvement of signal-to-noise ratio by stochastic resonance in sigmoid function threshold systems, demonstrated using a CMOS inverter

Stochastic resonance (SR) has become a well-known phenomenon that can enhance weak periodic signals with the help of noise. SR is an interesting phenomenon when applied to signal processing. Although it has been proven that SR does not always improve the signal-to-noise ratio (SNR), in a strongly nonlinear system such as simple threshold system, SR does in fact improve SNR for noisy pulsed signals at appropriate noise strength. However, even in such cases, when noise is weak, the SNR is degraded. Since the noise strength cannot be known in advance, it is difficult to apply SR to real signal processing. In this paper, we focused on the shape of the threshold at which SR did not degrade the SNR when noise was weak. To achieve output change when noise was weak, we numerically analyzed a sigmoid function threshold system. When the slope around the threshold was appropriate, SNR did not degrade when noise was weak and instead was improved at suitable noise strength. We also demonstrated SNR improvement for noisy pulsed voltages using a CMOS inverter, a very common threshold device. The input-output property of a CMOS inverter resembles the sigmoid function. By inputting the noisy signal voltage to a CMOS inverter, we measured the input and output voltages and analyzed the SNRs. The results showed that SNR was effectively improved over a wide range of noise strengths.     

基于经验模态分解滤波的低频振荡Prony分析

传统Prony法在分析低频振荡时对输入信号要求较高,存在着对噪声敏感的弱点.因此提出一种经验模态分解滤波和改进Prony法相结合的低频振荡分析方法.该方法先用经验模态分解对低频振荡信号进行自适应滤波,再用改进Prony法对滤波后的信号进行分析.其中,改进Prony法有效阶数用归一化奇异值法确定.将该方法分别用于分析试验信号和IEEE 4机系统振荡信号,并与基于低通滤波器的Prony分析进行比较.结果表明,在较大噪声环境下,该方法仍然能相对准确的辨识出低频振荡主导模式,验证了其有效性. 关键词： 低频振荡 经验模态分解 改进Prony法 归一化奇异值法     

Two approaches to multiple specular echo detection using split spectrum processing: moving bandwidth minimization and mathematical morphology

A split spectrum processing technique using a novel moving bandwidth minimization (MBM) method was developed to detect multiple specular targets having different spectral characteristics. Mathematical morphology (MM) algorithms were also implemented in order to compare the results. An experimental approach to optimal parameter determination is described. These non-linear filtering methods are applied to medical in vivo imaging to illustrate specular detection and signal to noise ratio (SNR) enhancement.     

Study on the denoising method for the electromagnetic ultrasonic echoes from multiple interfaces

The electromagnetic ultrasound is used in the detection of interfaces of the adhesive multilayer structures to solve the unstable coupling problem in ultrasonic testing by traditional piezoelectric transducers. Based on the analysis of the transforming mechanism of electromag-netic ultrasound energy and the resultant dead zone from mutual inductance of the transducer, the wavelet filtering by soft-thresholding and adaptive noise canceling methods are used simul-taneously to the detected electromagnetic ultrasonic signals to overcome the drawbacks of the low signal to noise ratio （SNR） and the wide intrinsic dead zone of the transducer. Processed results in the interface detection of a three layered adhesive sample of steel and rubber materials demonstrate that the wavelet filtering enhances the SNR about 12dB while the adaptive noise canceling narrows the dead zone effectively.     

Application of adaptive time-frequency decomposition in ultrasonic NDE of highly-scattering materials

In the paper, adaptive time-frequency decomposition by basis pursuit (BP) is utilized to improve ultrasonic flaw detection in highly-scattering materials as an alternative to the Wavelet Transform technique. The detection of ultrasonic pulses using the BP is described. Computer simulation was performed to verify the signal detection improvements for an ultrasonic wave embodied in white noise, and numerical results show good detection even for signal-noise ratio (SNR) of -18 dB. The improvement in detection is experimentally verified using cast steel samples with artificial flaws.     

Noise reduction of speech signals using time-varying and multi-band adaptive gain control for smart digital hearing protectors

In this paper, a single-channel speech enhancement algorithm based on non-linear and multi-band Adaptive Gain Control (AGC) is proposed. The algorithm requires neither Signal-to-Noise Ratio (SNR) nor noise parameters estimation. It reduces the background noise in the temporal domain rather than the spectral domain using a non-linear and automatically adjustable gain function for multi-band AGC. The gain function varies in time and is deduced from the temporal envelope of each frequency band to highly compress the frequency regions where noise is present and lightly compress the frequency regions where speech is present. Objective evaluation using the PESQ (Perceptual Evaluation of Speech Quality) metric shows that the proposed algorithm performs better than three benchmarks, namely: the spectral subtraction, the Wiener filter based on a priori SNR estimation and a band-pass modulation filtering algorithm. In addition, blind subjective tests show that the proposed algorithm introduces less musical noise compared to the benchmark algorithms and was preferred 78.8% of the time in terms of signal quality. The proposed algorithm is implemented in a miniature low power digital signal processor to validate its feasibility and complexity for smart hearing protection in noisy environments.     

一种自适应层进式Savitzky‐Golay光谱滤波算法及其应用

可调谐半导体激光吸收光谱技术（TDLAS）利用半导体激光器的可调谐和窄线宽特性,通过选择特定气体的单条吸收线,排除其余气体的干扰,可以实现高精度、高选择性的气体浓度测量,在气体浓度检测系统中具有广泛的应用前景。在不同的应用条件和环境下,需要解决相应的硬件和数据处理方面的技术问题。主要研究TDLAS技术机动车尾气CO组分浓度遥测系统中的光谱数据处理问题,该系统利用路面漫反射回波信号遥测行驶中的机动车尾气CO组分浓度。由于激光扫描光谱回波信号受到漫反射面情况变化、空气环境变化、尾气湍流影响等因素影响,探测器收集到的信号不仅较弱同时也夹杂着多种噪声, 即测量光路信噪比较差, 故提出一种自适应层进式Savitzky-Golay(S-G)平滑滤波算法,实现了对光谱进行滤波处理从而更加准确地反演CO浓度。S-G滤波算法因其原理简单、功能强大、只需设置两个参数（窗口大小、拟合阶数）等优点,已广泛应用于光谱处理。如何正确设置S-G算法参数使滤波效果在去噪不足和过度滤波之间找到平衡点,是该滤波算法应用的一大难题。设计的检测系统中,测量光路光谱信号为非平稳信号,噪声和有效信号幅度时变,最佳窗口大小和多项式阶数随信号动态而变化,且变化区间较大,使用固定参数的S-G滤波器难以达到最佳效果。提出的自适应层进式S-G平滑滤波算法,通过逐层将测量光路光谱信号经过S-G滤波后,与参考光路的光谱信号设置的参考段比对信号相关系数和信号一阶导相关系数的和,以自适应得到逐层最优参数。通过对信噪比从9.81～29.77的10组不同带噪光谱分析验证了该算法的有效性,自适应层进式S-G算法能较好地去除噪声并还原带噪信号所携带的待测气体浓度信息,与带噪光谱对比,吸收光谱峰值最大误差由25.152%降至5.917%,积分吸光度最大误差由18.1%降至3.9%。在实现的系统中,使用自适应层进式S-G算法对测量光路进行滤波处理,并对不同车型、不同排量、燃烧不同油品的机动车在怠速和缓速通过(5 km·h-1)系统时其排放的CO浓度进行实时在线监测。     

Time-domain noise reduction based on an orthogonal decomposition for desired signal extraction

This paper addresses the problem of noise reduction in the time domain where the clean speech sample at every time instant is estimated by filtering a vector of the noisy speech signal. Such a clean speech estimate consists of both the filtered speech and residual noise (filtered noise) as the noisy vector is the sum of the clean speech and noise vectors. Traditionally, the filtered speech is treated as the desired signal after noise reduction. This paper proposes to decompose the clean speech vector into two orthogonal components: one is correlated and the other is uncorrelated with the current clean speech sample. While the correlated component helps estimate the clean speech, it is shown that the uncorrelated component interferes with the estimation, just as the additive noise. Based on this orthogonal decomposition, the paper presents a way to define the error signal and cost functions and addresses the issue of how to design different optimal noise reduction filters by optimizing these cost functions. Specifically, it discusses how to design the maximum SNR filter, the Wiener filter, the minimum variance distortionless response (MVDR) filter, the tradeoff filter, and the linearly constrained minimum variance (LCMV) filter. It demonstrates that the maximum SNR, Wiener, MVDR, and tradeoff filters are identical up to a scaling factor. It also shows from the orthogonal decomposition that many performance measures can be defined, which seem to be more appropriate than the traditional ones for the evaluation of the noise reduction filters.     

一种改进的微弱NQR信号检测算法

核四极矩共振（NQR）是一种固态射频谱分析技术,可用于检测高危险爆炸物. 然而NQR信号本身非常弱,并且易受线圈的热噪声和外部射频干扰的影响,低信噪比限制了NQR的实际应用. 该文提出一种改进的微弱NQR信号检测算法. 首先利用Hankel矩阵方式下奇异值分解的方法,有效地抑制射频干扰和噪声,并将NQR信号分离出来. 然后提出了一种基于MUSIC谱估计的非线性最小二乘检测器,它既保证了高的频率分辨率,又大大降低了运算量. 仿真数据和实测数据结果表明该算法的有效性.     

A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.     

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.     

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.     

Speech enhancement based on the discrete Gabor transform and multi-notch adaptive digital filters

This paper presents a new method to speech enhancement based on time-frequency analysis and adaptive digital filtering. The proposed method for dual-channel speech enhancement was developed by tracking frequencies of corrupting signal by the discrete Gabor transform (DGT) and implementing multi-notch adaptive digital filter (MNADF) at those frequencies. Since no a priori knowledge of the noise source statistics is required this method differs from traditional speech enhancement methods. Specifically, the proposed method was applied to the case where speech quality and intelligibility deteriorate in the presence of background noise. Speech coders and automatic speech recognition (ASR) systems are designed to act on clean speech signals. Therefore, corrupted speech signals by the noise must be enhanced before their processing. The method uses a primary input containing the corrupted speech signal while a reference input containing the noise only. In this paper, we designed MNADF instead of single-notch adaptive digital filter and used DGT to track frequencies of corrupting signal because fast filtering process and fast measure of the time-dependent noise frequency are of great importance in speech enhancement process. Therefore, MNADF was implemented to take advantage of fast filtering process. Different types of noises from Noisex-92 database were used to degrade real speech signals. Objective measures, the study of the speech spectrograms and global signal-to-noise ratio (SNR), segmental SNR (segSNR), Itakura-Saito distance measure as well as subjective listing test demonstrated consistently superior enhancement performance of the proposed method over traditional speech enhancement method such as spectral subtraction. Combining MNADF and DGT, excellent speech enhancement was obtained.