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.     

Detection-recognition algorithm based on the Gabor transform for unknown signals embedded in unknown noise

In this paper we present the CFAR (Constant False Alarm Rate) two-step detection-recognition algorithm for unknown, non-stationary signals embedded in unknown noise, based on the discrete Gabor transform. In the detection step, the decision about the absence or the presence of a signal of interest in a background of noise should be taken. The term ‘recognition’ means recovering the signal waveform from a noisy signal after the detection step. The recognition can be reformulated as the non-stationary, time-varying filtering problem in a time–frequency domain. In this paper the Gabor time–frequency domain is taken into account and the Gabor transform is used both in the detection and the in the filtering step. The discrete Gabor transform (DGT) is under intensive study of mathematicians, what results in a number of new, efficient computational algorithms for long time series. The Gabor frame approach is used for computation analysis and synthesis windows. Data-driven approach to develop the detection-recognition algorithm is based on the assumption, that disturbing noise signal after the Gabor transform, can be successfully approximated by the Weibull distribution regardless noise distribution before the transformation. It is shown by intensive simulations, that a two-parameter model like the Weibull distribution is really appropriate. Scale and shape parameters of the Weibull distribution are easily estimated and the CFAR threshold used in detection, based on estimated parameters, can be computed. The case of a low SNR ratio, with additional assumption about a signal, is also considered. It is shown that the iterative form of the time-varying filtering, significantly improves the quality of the whole detection-recognition CFAR algorithm. This approach is successfully investigated on a real-life radar signal.     

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.     

Matrix pencil method for simultaneously estimating azimuth and elevation angles of arrival along with the frequency of the incoming signals

In this paper we describe a method for simultaneously estimating the direction of arrival (DOA) of the signal along with its unknown frequency. In a typical DOA estimation problem it is often assumed that all the signals are arriving at the antenna array at the same frequency which is assumed to be known. The antenna elements in the array are then placed half wavelength apart at the frequency of operation. However, in practice seldom all the signals arrive at the antenna array at a single pre-specified frequency, but at different frequencies. The question then is what to do when there are signals at multiple frequencies, which are unknown. This paper presents an extension of the matrix pencil method to simultaneously estimate the DOA along with the operating frequency of each of the signals. This novel approach involves approximating the voltages that are induced in a three-dimensional antenna array, by a sum of complex exponentials by jointly estimating the direction of arrival (both azimuth and elevation angles) along with the carrier frequencies of multiple far-field sources impinging on the array by using the three-dimensional matrix pencil method. The matrix pencil method is a direct data domain method for approximating a function by a sum of complex exponentials in the presence of noise. The variances of the estimates computed by the matrix pencil method are quite close to the Cramer–Rao bound. Finally, we illustrate how to carry out the broadband DOA estimation procedure using realistic antenna elements located in a conformal array. Some numerical examples are presented to illustrate the applicability of this methodology in the presence of noise. It is shown that the variance decreases as the SNR increases. The Cramer–Rao bound for the estimators are also provided to illustrate the accuracy and the computational efficiency of this new methodology.     

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.     

Multi notch adaptive digital filter design for enhancement of speech signals embedded in non-stationary noise

This paper presents a new approach to speech enhancement based on modified least mean square-multi notch adaptive digital filter (MNADF). This approach differs from traditional speech enhancement methods since no a priori knowledge of the noise source statistics is required. Specifically, the proposed method is applied to the case where speech quality and intelligibility deteriorates in the presence of background noise. Speech coders and automatic speech recognition systems are designed to act on clean speech signals. Therefore, corrupted speech signals by the noise must be enhanced before their processing. The proposed method uses a primary input containing the corrupted speech signal and a reference input containing noise only. The new computationally efficient algorithm is developed here based on tracking significant frequencies of the noise and implementing MNADF at those frequencies. To track frequencies of the noise time-frequency analysis method such as short time frequency transform is used. Different types of noises from Noisex-92 database are used to degrade real speech signals. Objective measures, the study of the speech spectrograms and global signal-to-noise ratio (SNR), segmental SNR (segSNR) as well as subjective listing test demonstrate consistently superior enhancement performance of the proposed method over tradition speech enhancement method such as spectral subtraction.     

基于EMD和互信息熵的微震信号自适应去噪

针对强干扰背景下的微震信号提取,提出一种基于经验模态分解（Empirical Mode Decomposition,EMD）和互信息熵的自适应提取算法。通过EMD对微震信号进行分解,得到高频和低频两部分信号,并对分解得到的各阶固有模态分量求出能量和能量熵值。根据互信息准则,通过依次计算相邻分量能量熵之间的互信息值来区分高频和低频信号。将经过自适应阈值滤波后的高频信号和低频信号一起进行信号重构,得到新的微震信号。仿真结果表明,在对微震信号去噪时,该方法可以有效地去除噪声信号,信噪比均提升了10 dB以上。工程上的微震信号通过该方法处理后,也取得了较好的效果。     

基于自适应陷波的非均匀采样信号处理系统

非均匀采样可以突破奈奎斯特采样定理的限制,检测出超过采样频率的信号频率,但非均匀采样引起信号的频谱噪声,使得非均匀采样的小信号检测难于实现。研制了一种实时非均匀采样信号处理系统,采用自适应陷波方法计算非均匀采样信号的频率,逐步滤除幅度较大的信号,从而检测出小幅度信号。详细说明了自适应陷波方法的原理和实现方法,并介绍了基于数字信号处理器(DSP)的非均匀采样信号处理系统。     

基于数据复制和数字上变频的高速信号的产生

利用IQ数字上变频器AD9957,将高速DSP产生的基带信号上变到中频,再用混频器将中频变到需要的微波频段。对于基带信号的产生,高速存储器的数据复制和数字上变频技术是关键。对杂散和杂散抑制进行了分析。经过测试,本系统能够产生单音、多音和线性调频信号,调频中心频率达4.3 GHz,带宽大于10 MHz。     

Time–frequency analysis of signals using support adaptive Hermite–Gaussian expansions

Since Hermite–Gaussian (HG) functions provide an orthonormal basis with the most compact time–frequency supports (TFSs), they are ideally suited for time–frequency component analysis of finite energy signals. For a signal component whose TFS tightly fits into a circular region around the origin, HG function expansion provides optimal representation by using the fewest number of basis functions. However, for signal components whose TFS has a non-circular shape away from the origin, straight forward expansions require excessively large number of HGs resulting to noise fitting. Furthermore, for closely spaced signal components with non-circular TFSs, direct application of HG expansion cannot provide reliable estimates to the individual signal components. To alleviate these problems, by using expectation maximization (EM) iterations, we propose a fully automated pre-processing technique which identifies and transforms TFSs of individual signal components to circular regions centered around the origin so that reliable signal estimates for the signal components can be obtained. The HG expansion order for each signal component is determined by using a robust estimation technique. Then, the estimated components are post-processed to transform their TFSs back to their original positions. The proposed technique can be used to analyze signals with overlapping components as long as the overlapped supports of the components have an area smaller than the effective support of a Gaussian atom which has the smallest time-bandwidth product. It is shown that if the area of the overlap region is larger than this threshold, the components cannot be uniquely identified. Obtained results on the synthetic and real signals demonstrate the effectiveness for the proposed time–frequency analysis technique under severe noise cases.     

基于构造Hankel矩阵的SVD陷波方法*

提出一种新的通过加入引导信号构造Hankel矩阵经奇异值分解（SVD）滤除相应频率成分的陷波方法。根据待处理信号构造的Hankel矩阵,经SVD后其奇异值对应信号中不同频谱幅值的频率成分,提出加入某特定频率信号作为引导信号使得该频率成分成为信号中的主成分,形成易区分的奇异值对,在信号重构时除掉该奇异值对便可滤除相应频率成分。用本方法对脑磁信号进行50 Hz工频陷波处理,达到了很好的陷波效果,且该方法不受传统滤波器陷波越深受影响带宽越宽的限制。     

一种基于行列法离散点边界搜索算法*

平面上构建离散点的边界在地理信息系统(GIS)中应用广泛,提出了基于行列法的平面离散点边界搜索的新算法,目的是解决平面离散点边界问题,通过确定步长大小,按步长对离散点分别进行行搜索和列搜索,得到离散点的边界曲线,介绍了行列边界算法的基本思想和实现过程。该算法能够正确地搜索包含凹凸特征的离散点边界,与传统边界生成算法相比,它具有通用、实现简单等特点。该算法在油田GIS领域边界划分中得到了很好的应用,能够准确地构建油田边界。     

多抽样率Gabor变换并行算法的FPGA仿真和设计

Gabor变换在信号处理领域被公认为十分有效的时频分析方法,然而却因为Gabor变换算法具有较高的计算复杂性而限制了其实时应用,最近提出的基于多抽样率滤波实现离散Gabor变换的并行算法可很好地解决实时应用问题。讨论用FPGA来实现多抽样率Gabor变换并行算法的仿真,并运用Quartus II 9.0和modelsim等软件以及Verilog硬件描述语言来辅助设计。     

Filterbank optimization for robust ASR using GA and PSO

Automatic speech recognition (ASR) systems follow a well established approach of pattern recognition, that is signal processing based feature extraction at front-end and likelihood evaluation of feature vectors at back-end. Mel-frequency cepstral coefficients (MFCCs) are the features widely used in state-of-the-art ASR systems, which are derived by logarithmic spectral energies of the speech signal using Mel-scale filterbank. In filterbank analysis of MFCC there is no consensus for the spacing and number of filters used in various noise conditions and applications. In this paper, we propose a novel approach to use particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the parameters of MFCC filterbank such as the central and side frequencies. The experimental results show that the new front-end outperforms the conventional MFCC technique. All the investigations are conducted using two separate classifiers, HMM and MLP, for Hindi vowels recognition in typical field condition as well as in noisy environment.     

An approach to classification and under-sampling of the interfering wireless signals

Classification of interfering signals that belong to different wireless standards is important topic in wireless communications. In this paper, we propose a procedure for separation and classification of wireless signals belonging to the Bluetooth and to the IEEE 802.11b standards. These signals operate in the same frequency band and may interfere with each other. The procedure is made of a few steps. In the first step, the separation of signal components is done using the eigenvalue decomposition approach. The second stage is based on the compressive sensing approach, used to reduce the number of transmitted samples. A suitable transform domain is chosen for each separated component using ℓ₁-norm as a measure of sparsity. Since the Bluetooth signals are less sparse compared to the IEEE 802.11b signals, after choosing sparse domain, additional sparsification needs to performed to further enhance the sparsity. In the last step of the procedure, the classification is performed by observing the time-frequency characteristics of the reconstructed separated components. The theory is proved by the experimental results.     

基于独立成分分析的表面肌电信号工频去噪

表面肌电信号(SEMG)采集中,如何消除工频干扰对信号的后续应用意义重大.在探讨独立成分分析(ICA)原理的基础上,提出了一种用于表面肌电信号工频去噪的快速独立成分分析(FastICA)算法.该方法通过对观测信号去均值和白化处理后,用负熵作判据通过迭代得到解混矩阵,经解混运算得到源信号.针对混合信号ICA分离效果的差异,引入最大似然指标作为分离效果的评价量.实验结果表明,所提算法能有效分离SEMG信号中的工频噪声,运用最大似然评价指标将工频噪声降至最低.     

基于稀疏分解的超声无损检测信号处理

为了在复杂背景噪声情况下,对缺陷的大小和位置实现精确的识别,提出了一种基于Ga-bor原子库稀疏分解的信号处理方法 ,利用匹配追踪算法将信号在超完备Gabor原子库中进行稀疏表示。采用相干比阈值作为迭代终止条件,根据信号噪声水平自适应调整迭代次数。针对算法计算量大的缺点,引入遗传算法,大大提高了计算的效率。实验表明,该方法可以有效减小噪声的影响,具有计算效率高、稳定性好的特点。     

Sinusoidal model based on instantaneous frequency attractors

We propose a sinusoidal synthesis method based on instantaneous frequency (IF) attractors, which correspond to harmonic frequency trajectories. The algorithm is novel in extracting accurate sinusoidal components. Since the continuity of IF attractors is well-defined and simple to detect, the IF attractors can be extracted from audio signals without any explicit constraint or complicated algorithm; therefore, they can be directly applied to sinusoidal synthesis. Accuracy of IF attractor analysis can be further improved by time-warping analysis, which improves resolution for harmonic components whose frequencies that change rapidly. We describe the procedure for sinusoidal synthesis and precise phase estimation along with examples and also evaluate the effect of time-warping on analysis and synthesis of speech.     

抑制心电信号中工频干扰的滤波采样系统设计

针对心电信号采集过程中易受工频信号干扰的问题,设计了一种高衰减倍数的滤波采样系统.以XC3SD3400A芯片为主控单元,陷波模块和滤波模块依次对心电信号滤波,采用16位AD7656采样滤波,并将数据缓存后通过通用串口总线(USB)传输至上位机处理.定性和定量分析结果表明:系统能够将工频噪声衰减300倍,且输出信噪比有较大的提升,具有噪声高衰减、有用信息低损失、高精度采样等优点.     

Speech enhancement based on undecimated wavelet packet-perceptual filterbanks and MMSE–STSA estimation in various noise environments

In this paper, we proposed a new speech enhancement system, which integrates a perceptual filterbank and minimum mean square error–short time spectral amplitude (MMSE–STSA) estimation, modified according to speech presence uncertainty. The perceptual filterbank was designed by adjusting undecimated wavelet packet decomposition (UWPD) tree, according to critical bands of psycho-acoustic model of human auditory system. The MMSE–STSA estimation (modified according to speech presence uncertainty) was used for estimation of speech in undecimated wavelet packet domain. The perceptual filterbank provides a good auditory representation (sufficient frequency resolution), good perceptual quality of speech and low computational load. The MMSE–STSA estimator is based on a priori SNR estimation. A priori SNR estimation, which is a key parameter in MMSE–STSA estimator, was performed by using “decision directed method.” The “decision directed method” provides a trade off between noise reduction and signal distortion when correctly tuned. The experiments were conducted for various noise types. The results of proposed method were compared with those of other popular methods, Wiener estimation and MMSE–log spectral amplitude (MMSE–LSA) estimation in frequency domain. To test the performance of the proposed speech enhancement system, three objective quality measurement tests (SNR, segSNR and Itakura–Saito distance (ISd)) were conducted for various noise types and SNRs. Experimental results and objective quality measurement test results proved the performance of proposed speech enhancement system. The proposed speech enhancement system provided sufficient noise reduction and good intelligibility and perceptual quality, without causing considerable signal distortion and musical background noise.