基于Hilbert-Huang变换的语音信号分离

针对短时傅里叶变换不能正确得到非平稳信号的能量频率分布问题,提出了一种基于Hilbert-Huang变换的单信道语音信号分离的算法。该算法首先对分解得到的各内蕴模式函数分量(IMF)进行Hilbert变换,得到混合信号时频面上的Hilbert谱,然后对混合信号的Hilbert谱运用独立子空间分析的方法得出代表各个独立源信号的子空间,并对其求逆变换,从而恢复出各个源信号。通过仿真实验验证了此算法的正确性和有效性,且与短时傅里叶变换时频分析法相比较,其分离性能明显得到改善,显示了Hilbert-Huang变换在处理非平稳信号的优越性。     

Controlled-coverage discrete S-transform (CC-DST): Theory and applications

The S-transform (ST) is a popular linear time-frequency (TF) transform with hybrid characteristics from the short-time Fourier transform (STFT) and the wavelet transform. It enables a multi-resolution TF analysis and returns globally referenced local phase information, but its expensive computational requirements often overshadow its other desirable features. In this paper, we develop a fully discrete ST (DST) with a controllable TF sampling scheme based on a filter-bank interpretation. The presented DST splits the analyzed signal into subband channels whose bandwidths increase progressively in a fully controllable manner, providing a frequency resolution that can be varied and made as high as required, which is a desirable property for processing oscillatory signals lacked by previously presented DSTs. Thanks to its flexible sampling scheme, the behavior of the developed transform in the TF domain can be adjusted easily; with specific parameter settings, for example, it samples the TF domain dyadically, while by choosing different settings, it may act as a STFT. The spectral partitioning is performed through asymmetric raised-cosine windows whose collective amplitude is unitary over the signal spectrum to ensure that the transform is easily and exactly invertible. The proposed DST retains all the appealing properties of the original ST, representing a local image of the Fourier transform; it requires low computational complexity and returns a modest number of TF coefficients. To confirm its effectiveness, the developed transform is utilized for different applications using real-world and synthetic signals.     

Instantaneous frequency based spectral analysis of nuclear magnetic resonance spectroscopy data

Nuclear magnetic resonance spectroscopy signals are modelled as a sum of decaying complex exponentials in noise. The spectral analysis of these signals allowing for their decomposition and the estimation of the parameters of the components is crucial to the study of biochemical samples. This paper presents a novel Gabor filterbank/notch filtering instantaneous frequency (IF) estimator, that enables the extraction of weaker and shorter lived exponentials. This new approach is an iterative procedure where a Gabor filterbank is first employed to obtain a reliable estimate of the IF of the strongest component present. The estimated strongest component is then notch filtered, which un-masks weaker components, and the procedure repeated. The performance of this method was evaluated using an artificial signal and compared to the short time Fourier transform, reassigned STFT, and the original Gabor filterbank approach. The results clearly demonstrate its superiority in uncovering weaker signals and resolving components that are very close to one another in frequency. Furthermore, the new method is shown to be more robust than the ITCMP technique at low signal to noise ratios.     

A Robust Audio Watermark Representation Based on Linear Chirps

In this paper, we introduce a novel watermark representation for audio watermarking, where we embed linear chirps as watermark signals. Different chirp rates, i.e., slopes on the time–frequency (TF) plane, represent watermark messages such that each slope corresponds to a unique message. These watermark signals, i.e., linear chirps, are embedded and extracted using an existing watermarking algorithm. The extracted chirps are then postprocessed at the receiver using a line detection algorithm based on the Hough–Radon transform (HRT). The HRT is an optimal line-detection algorithm, which detects directional components that satisfy a parametric constraint equation in the image of a TF plane, even at discontinuities corresponding to bit errors. Simulation results show that HRT correctly detects the embedded watermark message after common signal processing operations for bit error rates up to 20%. The new watermark representation and the postprocessing stage based on HRT significantly improve the performance of the watermark detection process and can be combined with existing watermark embedding/extraction algorithms for increased robustness.     

颤振试验中加速度计信号的时频滤波方法研究

针对飞机颤振试飞试验数据信噪比偏低的问题,提出了两种加速度计信号的时频域滤波算法,分别借助小波和分数阶傅里叶变换对数据进行时频分析,并在时频域内滤波.该类算法的共同思想是利用扫频信号在时频域内的聚焦特性,有效提取真实响应信号,达到了信噪分离的目的.文中给出了具体的滤波算法,并通过仿真算例和实际试飞数据检验了滤波效果.结果表明两种方法均可显著提高加速度计信号的信噪比.其中,小波变换方法的通用性较好,而分数阶傅里叶变换在处理线性扫频激励数据时表现了更优的去噪效果.     

Low bit-rate speech coding based on multicomponent AFM signal model

In this paper, we propose a novel multicomponent amplitude and frequency modulated (AFM) signal model for parametric representation of speech phonemes. An efficient technique is developed for parameter estimation of the proposed model. The Fourier–Bessel series expansion is used to separate a multicomponent speech signal into a set of individual components. The discrete energy separation algorithm is used to extract the amplitude envelope (AE) and the instantaneous frequency (IF) of each component of the speech signal. Then, the parameter estimation of the proposed AFM signal model is carried out by analysing the AE and IF parts of the signal component. The developed model is found to be suitable for representation of an entire speech phoneme (voiced or unvoiced) irrespective of its time duration, and the model is shown to be applicable for low bit-rate speech coding. The symmetric Itakura–Saito and the root-mean-square log-spectral distance measures are used for comparison of the original and reconstructed speech signals.     

基于Curvelet变换的图像压缩感知重构

压缩感知主要采用离散余弦变换(DCT)和正交小波进行图像的稀疏表示,但是DCT时频分析性能不佳,小波方向选择性差,不能很好地表示图像边缘的信息。为此,利用Curvelet变换具有的多尺度、各向奇异性、更高稀疏表示性能等特性,提出基于Curvelet变换的图像压缩感知重构算法,采用Curvelet对图像进行稀疏表示和小波域阈值处理,以此解决信号重构噪声问题。实验结果证明,与传统小波变换和Contourlet变换相比,该算法在Lena图像上峰值信噪比平均提高了1.86 dB和1.15 dB。将Curvelet变换应用于压缩感知,能使图像边缘和平滑部分得到最优的表示,图像细节部分重构效果得到大幅提升,有效提高图像整体重构质量。     

Local instantaneous frequency estimation of multi-component signals

We present a method for estimating the instantaneous frequency (IF) of multi-component signals. This method involves the calculation of a time–frequency energy density of the signal, then obtaining a local IF estimate from this joint density. Time–frequency energy density is calculated as a least squares optimal combination of multi-window Gabor based evolutionary spectra. The optimal weights are obtained by minimizing an error criterion that is the difference between a reference time–frequency distribution and the combination of evolutionary spectra. IF of the signal components is estimated from the final evolutionary spectrum at small time–frequency regions as the average of frequencies at each time. As such, local IF information of a multi-component signal can be estimated in the time–frequency plane.     

基于小波变换和神经网络的车内噪声信号重构

为获取较高精度车内噪声主动控制(Active Noise Control, ANC)参考信号,提出了一种基于小波变换和BP神经网络的车内噪声信号重构方法。以在某轿车采集到的噪声信号为基础,用声学传递路径分析(TPA)方法确定影响车内噪声的关键点信号。鉴于噪声源信号对车内信号非线性关系的复杂性,建立BP神经网络的噪声重构模型,并利用小波分解来降低噪声信号的非平稳性。为对比重构效果,建立BP神经网络噪声重构模型。结果表明,本文提出算法的重构值与实测值之间的平均绝对误差比BP神经网络小,并且基于小波变换和BP网络重构模型的平均绝对误差均小于0.01。该方法能够对车内噪声信号进行准确、有效的重构。     

曲波域高斯混合尺度模型的图像压缩重构

压缩传感理论将信号的采样与压缩同时进行,利用信号在变换基上可以稀疏表示的先验知识,从比香农采样少的多的观测值中重构原始信号。近年来,两步迭代阈值算法作为一种求解反问题的优化方法,因其与多尺度几何分析存在紧密联系,且算法参数少,思想比较简单等特点,已经应用到了压缩重构中。但其使用时域的软硬阈值算子,不能获得很好的图像稀疏表示,从而使得算法重构精度不高。针对上述问题,本文在研究两步迭代阈值算法的基础上,提出了一种自适应的两步迭代阈值算法。该算法利用当前估计值提供的信息自适应估计步长参数,保证了估计值向最优解方向移动,提高了算法的重构精度,且针对其稀疏表示信号能力不足的缺点,运用高斯混合尺度模型对曲波邻域系数进行建模,充分利用曲波变换平移不变性和多方向选择性的优点,增加了图像表示的稀疏度。最后将其应用到图像压缩重构中,实验结果表明,该算法在峰值信噪比和主观视觉上都优于小波域高斯混合尺度模型和曲波硬阈值重构方法。     

Energy-Based Split Vector Quantizer Employing Signal Representation in Multiple Transform Domains

Mikhael, W., and Krishnan, V., Energy-Based Split Vector Quantizer Employing Signal Representation in Multiple Transform Domains, Digital Signal Processing11 (2001) 359–370Vector quantization schemes are widely used for waveform coding of one- and multidimensional signals. In this contribution, a novel energy-based, split vector quantization technique is presented, which represents digital signals efficiently as measured by the number of bits per sample for a predetermined signal reconstruction quality. In this approach, each signal vector is projected into multiple transform domains. In the learning mode, for a given transform domain representation, the transformed vector is split into subvectors (subbands) of equal average energy estimated from the transformed training vector ensemble. An equal number of bits is assigned to each subvector. A codebook is then designed for each equal energy subband of each transform domain representation. In the running mode, the coder selects codes from the domain that best represents the signal vector. The proposed multiple transform, split vector quantizer is developed and its performance is evaluated for both single-stage and multistage implementations. Several single transform vector quantizers for waveform coding exist, some of which employ energy-based bit allocation. Sample results using one-dimensional speech signals confirm the superior performance of the proposed scheme over existing single transform vector quantizers for waveform coding.     

基于HHT变换的起动电动机特征信号时频分析

针对车辆起动电动机电气和机械故障发生时特征信号的时变不平稳特性,进行了时频域分析处理,提出了利用现代信号处理方法对故障信号提取特征向量的方法,主要对起动电动机的电枢和轴承故障进行诊断。在构建电机故障测试实验平台的基础上,利用破坏性实验构造了故障类型,测取了电枢电流和振动信号,分别采用小波分析理论和HHT变换对信号进行分析,通过分解再重构的方式将信号分解成了频率由高到低的不同分量,并获得了故障的特征频率,提取了特征向量。实验结果表明,基于HHT变换的现代信号处理方法在处理时变非平稳信号方面比小波分析理论更具有自适应性,更易识别。     

Tamper recovery algorithm for digital speech signal based on DWT and DCT

It is a challenging work to design tamper recovery schemes for digital speech signal. Briefly, there are two problems need to be solved. One is that the signals used to tamper recovery are difficult to generate and embed, and the second is that it’s hard to tamper location precisely for attacked speech signal. In this paper, compression and reconstruction method based on discrete wavelet transform (DWT) and discrete cosine transform (DCT) is given, to obtain the compressed signals used to tamper recovery. And then frame number and compressed signals are embedded based on block-based method. Attacked signal can be located by frame number, and compressed signals are extracted and used to reconstruct the attacked signal. Theory analysis and experimental results indicate that the scheme proposed not only improves the accuracy of tamper localization, but also can reconstruct the attacked signals.     

Fault feature extraction of rolling element bearings based on wavelet packet transform and sparse representation theory

Extracting reliable features from vibration signals is a key problem in machinery fault recognition. This study proposes a novel sparse wavelet reconstruction residual (SWRR) feature for rolling element bearing diagnosis based on wavelet packet transform (WPT) and sparse representation theory. WPT has obtained huge success in machine fault diagnosis, which demonstrates its potential for extracting discriminative features. Sparse representation is an increasingly popular algorithm in signal processing and can find concise, high-level representations of signals that well matches the structure of analyzed data by using a learned dictionary. If sparse coding is conducted with a discriminative dictionary for different type signals, the pattern laying in each class will drive the generation of a unique residual. Inspired by this, sparse representation is introduced to help the feature extraction from WPT-based results in a novel manner: (1) learn a dictionary for each fault-related WPT subband; (2) solve the coefficients of each subband for different classes using the learned dictionaries and (3) calculate the reconstruction residual to form the SWRR feature. The effectiveness and advantages of the SWRR feature are confirmed by the practical fault pattern recognition of two bearing cases.     

基于改进MOD学习的滚动轴承振动信号稀疏表示

基于过完备字典的振动信号稀疏表示是滚动轴承信号研究的新热点。提出一种改进MOD字典学习的算法,并用于滚动轴承振动信号的稀疏表示。该方法基于MOD（Method of Optimal Direction）训练学习过程,通过构造分段重叠训练矩阵,能够得到更为稀疏的变换系数。相对DCT、FFT和未改进的处理方法,该方法得到的变换系数更稀疏。将该方法应用到基于压缩感知的滚动轴承振动信号处理,在相同的重构误差范围内,该方法所需要的观测数更少,计算量更小。     

A new implementation of discrete bionic wavelet transform: Adaptive tiling

Bionic wavelet transform (BWT) is a biomodel-based adaptive time–frequency analysis technique. Due to its nonlinearity, it is difficult to realize the inverse BWT. To solve this problem, this paper introduces a new implementation for the discrete BWT (DBWT). The T-function from BWT is used to split the dyadic tiling map of DWT to obtain an adaptive DBWT tiling of the time–frequency plane. Quadrature-mirror filters, organized as the DBWT tiling map, are then employed to decompose the signal. This DBWT implementation makes the distortionless signal reconstruction possible. DBWT was used to decompose both simulated signal and actual nonstationary signals. Results show that DBWT performs better than discrete wavelet transform in demonstrating a more concentrated coefficient distribution in time–frequency plane. This proposed DBWT implementation will make BWT more applicable for the future nonstationary signal analysis.     

Passive detection of accelerometer-recorded fetal movements using a time–frequency signal processing approach

This paper describes a multi-sensor fetal movement (FetMov) detection system based on a time–frequency (TF) signal processing approach. Fetal motor activity is clinically useful as a core aspect of fetal screening for well-being to reduce the current high incidence of fetal deaths in the world. FetMov are present in early gestation but become more complex and sustained as the fetus progresses through gestation. A decrease in FetMov is an important element to consider for the detection of fetal compromise. Current methods of FetMov detection include maternal perception, which is known to be inaccurate, and ultrasound imaging which is intrusive and costly. An alternative passive method for the detection of FetMov uses solid-state accelerometers, which are safe and inexpensive. This paper describes a digital signal processing (DSP) based experimental approach to the detection of FetMov from recorded accelerometer signals. The paper provides an overview of the significant measurement and signal processing challenges, followed by an approach that uses quadratic time–frequency distributions (TFDs) to appropriately deal with the non-stationary nature of the signals. The paper then describes a proof-of-concept with a solution consisting of a detection method that includes (1) a new experimental set-up, (2) an improved data acquisition procedure, and (3) a TF approach for the detection of FetMov including TF matching pursuit (TFMP) decomposition and TF matched filter (TFMF) based on high-resolution quadratic TFDs. Detailed suggestions for further refinement are provided with preliminary results to establish feasibility, and considerations for application to clinical practice are reviewed.     

复杂电磁环境下基于信号稀疏表示的干扰抑制与通信信号重构方法

在复杂电磁环境下,通信信号与干扰在时频域重叠而难以分离,针对这一问题,提出了一种基于信号稀疏表示的干扰抑制与通信信号重构方法.首先,通过K阶奇异值分解（K-Singular Value Decomposition,K-SVD）算法,分别构造通信信号与干扰的过完备子字典;然后,对过完备子字典进行联合构建联合字典,利用正交匹配追踪（Orthogonal Matching Pursuit,OMP）算法实现信号的分离和重构;最后,对时频重叠下的2ASK信号和2PSK信号的干扰抑制和重构过程进行了计算机仿真,再对OFDM信号的干扰抑制和信号重构过程进行了实验验证.仿真及实验结果表明：该方法可以实现时频重叠情况下通信信号的干扰抑制与信号重构.     

The instantaneous frequency of cardiovascular time series: a comparison of methods

The instantaneous frequency (IF) of cardiovascular time series is used to describe the time-varying spectral contents of the characteristic frequency bands that are of interest for psychophysiological and cardiovascular research. Four methods to compute IF of band-limited, monocomponent, and analytical cardiovascular time series were compared by means of simulated time series contaminated with additive noise. These four methods are: the method using the inverse Fourier transform of uncorrelated time-slices of the Wigner-Ville distribution, the discrete time-frequency transform, the circular mean direction of the time-slices of the Wigner-Ville distribution, and the central finite difference of the phase. The time resolution of the estimates is optimal and is inversely related to the bandwidth of the frequency components, as given by the uncertainty principle of Gabor. At periods in time where the signal fulfills the requirements of the model signal, the four estimates of IF are numerically equal; only the circular mean direction showed a slight deviation from the other estimates. Although the estimates of IF differ at sudden phase shifts at low amplitude, i.e. at points where the signal locally does not comply with the requirements of the model signal, overall the four methods produce comparable estimates of IF of a cardiovascular time series at an optimal time resolution.     

Hilbert-小波变换的齿轮箱故障诊断*

采用希尔伯特—小波变换对振动加速度传感器获取的齿轮箱振动响应信号进行特性分析。利用小波变换分解获得振动响应信号的各层高频信号小波系数和低频信号小波系数,对小波系数进行重构获得具有不同特征时间尺度的各高频信号和低频信号;再对分解的信号进行希尔伯特变换获得时频信息谱以提取系统的统计特征信息,实现监测齿轮运转工作状态,及时发现齿轮的早期故障,提高机械运行的安全性。仿真研究结果表明,小波变换分解和希尔伯特边际谱方法在故障信息诊断方面是可行和有效的,提高了故障检测的可靠性。