A new two‐microphone Gauss‐Seidel pseudo affine projection algorithm for speech quality enhancement

This study addresses the problem of speech quality enhancement by adaptive and nonadaptive filtering algorithms. The well‐known two‐microphone forward blind source separation (TM‐FBSS) structure has been largely studied in the literature. Several two‐microphone algorithms combined with TM‐FBSS have been recently proposed. In this study, we propose 2 contributions: In the first, a new two‐microphone Gauss‐Seidel pseudo affine projection (TM‐GSPAP) algorithm is combined with TM‐FBSS. In the second, we propose to use the new TM‐GSPAP algorithm in speech enhancement. Furthermore, we show the efficiency of the proposed TM‐GSPAP algorithm in speech enhancement when highly noisy observations are available. To validate the good performances of our algorithm, we have evaluated the adaptive filtering properties in computational complexity and convergence speed performance by system mismatch criteria. A fair comparison with adaptive and nonadaptive noise reduction algorithms are also presented. The adaptive algorithms are the well‐known two‐microphone normalized least mean square algorithm, and the recently published two‐microphone pseudo affine projection algorithm. The nonadaptive algorithms are the one‐microphone spectral subtraction and the two‐microphone Wiener filter algorithm. We evalute the quality of the output speech signal in each algorithm by several objective and subjective criteria as the segmental signal‐to‐noise ratio, cepstral distance, perceptual evaluation of speech quality, and the mean opinion score. Finally, we validate the superior performances of the proposed algorithm with physically measured signals.     

Exploiting all combinations of microphone sensors in overdetermined frequency domain blind separation of speech signals

A new approach to overdetermined frequency domain blind source separation (BSS) of speech signals which exploits all combinations of observations and hence varying inter microphone spacings is proposed. The observations are divided into subgroups so that conventional frequency domain BSS algorithms can be used. By evaluating the separation performance obtained from each group on the basis of approximately measuring the independence of separated signals, the output of the group that has the best performance among all groups on a frequency‐by‐frequency basis is chosen as the overall output. The separated signals of the overall system are then obtained by transforming their frequency domain representations into the time domain. Simulation results based on speech signals confirm that the proposed approach has better performance based on the performance index (PI) as compared with a conventional scheme using only one microphone group and an existing overdetermined frequency domain BSS algorithm. Copyright © 2010 John Wiley & Sons, Ltd.     

基于小波分量奇异值分解的单通道盲分离算法

针对单通道盲分离领域先验信息不足的问题,提出了一种基于小波变换、奇异值分解和独立分量分析的单通道盲分离算法.首先对单路传感器接收的信号进行小波分解和重构,得到一组代表原始信号特征的细节信号,对其施加奇异值分解并剔除小于门限值的奇异点,以此消除干扰信号和噪声的影响.然后将经过处理的细节信号组作为独立分量分析算法的输入信号...     

An accurate forearm EMG signal classification method using two‐channel electrode

An accurate electromyography (EMG) classification algorithm to control a virtual hand prosthesis with 12 degrees of freedom using two surface EMG electrodes is presented in this paper. We propose the application of independent component analysis (ICA) for blind‐source separation of the EMG signals obtained from two electrodes. One of the problems affecting the EMG classification accuracy is the location dependence of the EMG signal due to the superposition of signals from multiple sources. ICA is used to separate the two signals obtained from two surface electrodes into two independent EMG signals prior to the feature extraction and classification processes. We demonstrate that the EMG classification accuracy can be improved using the ICA algorithm. We also propose a novel eigen‐based feature that is extracted from the short‐time Fourier transform (STFT) magnitude spectrum. Our new feature not only decreases feature dimensions but also performs better than other well‐known features. We also implement the EMG classification scheme on the virtual robot arm. The performance shows promising result as indicated by a decrease in the Davies–Bolden (DB) index after applying the ICA © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Feature extraction by supervised independent component analysis based on category information

Independent component analysis (ICA) is a technique of transforming observation signals into their unknown independent components; hence, ICA has often been applied to blind signal separation problems. In this application, it is expected that the obtained independent components extract essential information of independent signal sources from input data in an unsupervised fashion. Based on such characteristics, ICA is currently utilized as a feature extraction method for images and sounds for recognition purposes. However, since ICA is an unsupervised learning, the obtained independent components are not always useful in recognition. To overcome this problem, we propose a supervised approach to ICA using category information. The proposed method is implemented in a conventional three‐layered neural network, but its objective function to be minimized is defined not only for the output layer but also for the hidden layer. The objective function consists of the following two terms: one evaluates the kurtosis of hidden unit outputs and the other evaluates the error between output signals and their teacher signals. The experiments are performed using several standard datasets to evaluate performance of the proposed algorithm. It is confirmed that a higher recognition accuracy is attained by the proposed method as compared with a conventional ICA algorithm. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 161(2): 25–32, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20522     

Blind separation of convolutive mixtures of cyclostationary signals

An adaptive blind source separation algorithm for the separation of convolutive mixtures of cyclostationary signals is proposed. The algorithm is derived by applying natural gradient iterative learning to a novel cost function which is defined according to the wide sense cyclostationarity of signals and can be deemed as a new member of the family of natural gradient algorithms for convolutive mixtures. A method based on estimating the cycle frequencies required for practical implementation of the proposed algorithm is presented. The efficiency of the algorithm is supported by simulations, which show that the proposed algorithm has improved performance for the separation of convolved cyclostationary signals in terms of convergence speed and waveform similarity measurement, as compared to the conventional natural gradient algorithm for convolutive mixtures. Copyright © 2004 John Wiley & Sons, Ltd.     

Large‐scale super‐Gaussian sources separation using Fast‐ICA with rational nonlinearities

Independent component analysis (ICA) is one of the most powerful methods for solving blind source separation problem. In various ICA methods, the Fast‐ICA is an excellent algorithm, and it finds the demixing matrix that optimizes the nonlinear contrast function. There are three original contrast functions in the Fast‐ICA to separate super‐Gaussian and sub‐Gaussian sources, and their respective derivatives are similar to nonlinearities used in neural networks. For the separation of large‐scale super‐Gaussian sources, however, the contrast functions and the nonlinearities are not optimal owing to high computational cost. To solve this potential problem, this paper proposes four rational polynomial functions to replace the original nonlinearities. Because the rational polynomials can be quickly evaluated, when they are used in the Fast‐ICA, the computational load of the algorithms can be effectively reduced. The proposed rational functions are derived by the Pade approximant from Taylor series expansion of the original nonlinearities. To reduce the error of approximation, we make the behaviors of rational functions approach that of the original ones within an effective range as well as possible. The simulation results show that the Fast‐ICA algorithms with rational nonlinearities not only can speed up the convergence but also improve the separation performance of super‐Gaussian blind source separation. Copyright © 2016 John Wiley & Sons, Ltd.     

基于ICA盲源分离的声表面波无线传感器信号抗干扰方法

针对声表面波传感器无线信号易受环境中同频信号干扰的问题,设计了基于独立分量分析(ICA)盲源分离的抗干扰算法。该算法对传感器和同频干扰的混合信号进行分离,然后基于分离信号波形的衰减和等幅特征对信号进行判别。MATLAB仿真结果表明,算法能够有效分离混合信号并且保留了源信号的时域波形特征和频域信息。将抗干扰算法在基于数字信号处理器的信号采集和处理平台上实现并进行分离实验,设置不同的传感器信号和干扰信号强度并记录分离和判别结果,实验结果表明,在源信号的信号强度较为接近(信扰比在0.8~1.4范围内)且噪声影响可忽略的情况下,可达到95%以上的传感器信号准确判别率,有效地抑制了同频干扰。     

基于改进型象群优化算法的 BSS 方法

为了克服传统群智能算法在求解盲源分离(BSS)问题时收敛速度慢和分离精度差的缺点,提出一种基于改进型象群优化(IEHO)算法的BSS方法.该方法利用独立性原则,融合分离信号的峭度和互信息来构建目标函数.在氏族更新阶段,通过改进算法比例因子并加入邻域搜索,提高了算法搜索方式的多样性;在分离阶段,引入量子粒子群优化策略,提高了算法的全局搜索能力.仿真结果表明,与传统的象群优化算法和粒子群优化算法相比,IEHO算法的寻优效果较好,并成功实现了图像信号和语音信号的盲源分离,分离精度更高,收敛速度更快.     

Predictor‐based iterative neural dynamic surface control for three‐phase voltage source PWM rectifier

This paper addresses the control problem of a three‐phase voltage source pulse width modulation rectifier in the presence of parametric uncertainties and external time‐varying disturbances. An adaptive controller is designed by combining a modified dynamic surface control method and a predictor‐based iterative neural network control algorithm. Especially, neural networks with iterative update laws based on prediction errors are employed to identify the lumped uncertainties. Besides, a finite‐time‐convergent differentiator, instead of a first‐order filter, is used to obtain the time derivative of the virtual control law. Using a Lyapunov–Krasovskii functional, it is proved that all signals in the closed‐loop system are ultimately uniformly bounded. Both simulation and experimental studies are provided to show the effectiveness of the proposed approach. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

迭代ICA与LEVKOV联合的瞬态响应工频干扰抑制方法

针对传感器瞬态响应中的工频干扰因频率游动、谐波及频带混叠导致难以去除的问题,提出了迭代独立分量分析 (ICA)与 LEVKOV 联合抗工频干扰的方法。 将迭代 ICA 法与 LEVKOV 法相结合,通过构造工频参考信号,采用迭代 ICA 的方 法以减小瞬态响应中频率游动的工频干扰,再利用 LEVKOV 法通过线性段均值滤波和非线性段噪声模板滤波来进一步削弱工 频干扰及其谐波干扰,从而提高瞬态响应信号中工频干扰的滤除精度。 仿真和实验数据验证结果表明,所提出的迭代 ICA 与 LEVKOV 联合抗工频干扰法相较于独立的迭代 ICA 法和 LEVKOV 法而言,对传感器瞬态响应中的工频干扰具有更好的抑制 效果。     

Algorithm for detecting frequencies in multi‐sine signals by inductive generation

Prony's method is an approximation approach to decomposing a function into sum of exponents and thus is applicable to unknown frequencies estimation of signals. The concrete algorithms for estimating pure sine wave, triple tone, and quadruple tone have already been derived and presented. This paper aims at deriving the estimating algorithm for multi‐sine signals which consist of unknown sine waves. The new method of generating an algebraic algorithm for detecting unknown frequencies in the signals is derived by mathematical induction. The crux of the generation is dependent on the integer matrices induction. A handy method for generating the matrices is shown as well. Algorithms for the triple tone, quadruple tone, and the higher‐order tone are generated and verified. As a result, they are shown to be identical to the ordinary algorithms. Subjects on the application of the induced algorithm to practical frequency detection are discussed. The algorithm has both instantaneity in the time domain and higher resolution in the frequency domain, that is, the signal analysis by the algorithm can be performed without constraint of the uncertainty principle. An iterative solution for algebraic equation is dominant for calculation in the algorithm. Techniques for detecting frequencies in a multi‐sine of unknown order are also discussed. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 160(3): 27– 38, 2007; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.20404     

LCMV分频的改进维纳滤波后置波束形成算法

针对封闭环境中语音信号受到混响影响,提出了LCMV分频的改进维纳滤波后置波束形成算法。该算法通过计算麦克风阵列接收到含混响信号的短时傅里叶变换得到频域阵列信号,对频域阵列信号分频处理,将分频的信号进行线性约束最小方差波束形成滤波处理,该波束滤波根据每个频段上混响时间不同的特性对频域阵列信号进行分频处理后,将波束形成算法分别应用到高低频中,以提高混响抑制的精度;再由频域阵列信号的组合功率谱进行维纳后置滤波以抑制混响,由麦克风阵列接收到混响信号的直达波和反射波之间不相关性及麦克风阵列接收信号的空间信息解决维纳滤波器的精确估计问题;最后由逆短时傅里叶变换恢复出时域信号。仿真结果表明,该算法对混响抑制具有明显的改善;且在混响时间600 ms条件下语音增强系统的PESQ值提高了0.26。     

Application of the Variational Mode Decomposition for Power Quality Analysis

Harmonics and interharmonics in power systems distort the grid voltage, deteriorate the quality and stability of the power grid. Therefore, rapid and accurate harmonic separation from the grid voltage is crucial to power system. In this article, a variational mode decomposition-based method is proposed to separate harmonics and interharmonics in the grid voltage. The method decomposes the voltage signal into fundamental, harmonic, interharmonic components through the frequency spectrum. An empirical mode decomposition (EMD) and an ensemble empirical mode decomposition (EEMD) can be combined with the independent component analysis (ICA) to analyze the harmonics and intherharmonics. By comparing EMD-ICA, EEMD-ICA methods, the proposed method has several advantages: (1) a higher correlation coefficient of all the components is found; (2) it requires much less time to accomplish signal separation; (3) amplitude, frequency, and phase angle are all retained by this method. The results obtained from both synthetic and real-life signals demonstrate the good performance of the proposed method.     

An efficient search algorithm for ISF vector quantization in AMR‐WB speech codec

The adaptive multi‐rate wideband (AMR‐WB) speech codec with a sampling rate of 16 kHz is one of the speech codecs applied to 4G mobile communication systems as a way to remarkably improve the speech quality of a smartphone. However, a major drawback is that the vector quantization of the immittance spectral frequency (ISF) coefficients takes up the second largest of the total computational load of the AMR‐WB encoder. In other words, the speech quality is improved at the cost of high battery power consumption. Accordingly, this letter presents a triangular inequality elimination algorithm equipped with a dynamic and an intersection mechanism, named DI‐TIE, as a means to considerably improve the performance of ISF coefficient quantization in AMR‐WB speech codec. Both mechanisms are designed in a way that recursively enhances the performance of TIE algorithm. This proposal is demonstrated by experimental results as a superior search algorithm relative to a conventional TIE and a multiple TIE (MTIE) approaches. With a full search algorithm as a benchmark for search load comparison, this work provides a search load reduction of more than 77%, a figure far beyond the 36% in the TIE and 49% in the MTIE approach. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Application of the wavelet transform to the low‐bit‐rate speech coding system

In the analysis‐synthesis coding of speech signals, realization of high quality in the low‐bit‐rate coding depends on the extraction of its characteristic parameters in the preprocessing. The precise extraction of the fundamental frequency, one of the parameters of the source information, guarantees quality in speech synthesis. But its extraction is difficult because of the influence of the consonant, nonperiodicity of vocal cord vibrations, the wide range of the fundamental frequency, and so on. In this paper we propose a new fundamental frequency extraction with the criterion based on its harmonics structure and low‐bit‐rate speech coding system using the wavelet transform. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 148(3): 62–71, 2004; Published online in Wiley InterScience ( www.interscience.wiley.com ). DOI 10.1002/eej.10309     

Independent signal separation using genetic algorithm

Growing multimedia systems require more efficient signal separation methods to preserve quality of voice or music recording in a noisy environment. Some signal separation methods are based on minimizing the dependence measure among input signals to separate the noise component since the noise component is usually independent of the other signals. Under such circumstances, we have developed a new method to separate independent signal components which directly minimizes the Kullback–Leibler divergence by a genetic algorithm. In this paper, we have improved the method in its separation performance and processing speed. The simulation results show that the proposed method is effective in separating the independent signals. © 2000 Scripta Technica, Electr Eng Jpn, 131(4): 52–57, 2000     

基于ICA的含噪电力系统信号的频率测量

为提高在噪声环境下频率测量的准确性,提出一种结合独立分量分析(ICA)和加汉宁窗插值算法的含噪电力系统信号的频率测量方法.该方法使用ICA对混有噪声的信号进行分离获得电力系统信号后,使用加汉宁窗插值的傅里叶算法获得电力系统频率.由于ICA对噪声和有用信号进行了很好的分离,因此频率测量的精确度得到了显著提高.仿真分析了含有白噪声和脉冲噪声的情况.即使噪声的幅值大于信号幅值数百倍时,使用所提出算法后频率误差绝对值的最大值从2.4 Hz左右分别减少到了0.0028 Hz和0.0005 Hz,表明所提出算法在极低信噪比时仍具有较高的精确度.     

Extraction of Alpha Activities from an EEG Obtained During Gum Chewing

We propose a method for extracting alpha activities from electroencephalogram (EEG) recordings contaminated by noise from sources such as electromyograms (EMGs), electro‐oculograms (EOGs), or blinking, using independent component analysis (ICA) with simple preprocessing. In the preprocessing, Gaussian signals are connected to the back and front of the original EEG data. Then waveforms of the original EEG are not subjected to any change in shape. In this paper, we extract alpha activities from an EEG recorded while a subject chews gum. As a result, the ICA separation accuracy is often improved, and alpha activities during gum chewing can be successfully extracted. Furthermore, we investigate whether or not the extracted signals satisfy the general characteristics of an alpha rhythm. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于独立分量分析的电力系统瞬时电压畸变信号判别方法

提出一种基于独立分量分析(independent component analysis,ICA)的瞬时电能质量扰动信号检测与判别方法。利用ICA可将相互独立的源信号从其线性混合信号中分离出来的特点,以负熵作为衡量信号独立性的目标函数,通过优化此函数,得到一种固定点算法：FastICA算法,用此算法对包含瞬时电能质量扰动信号的电网电压信号进行计算,可分离出与扰动相对应的信号。对于不同类型的扰动,分离出的信号具有不同的波形特征,根据这一特点,可对扰动进行判别并确定其位置和持续时间。仿真试验结果表明,该方法对瞬时电压跌落、瞬时电压上升、瞬时脉冲、瞬时电压中断、谐波等多种瞬时电压畸变信号有较好的检测与判别效果。