Audio signal separation is an open and challenging issue in the classical“Cocktail Party Problem”.Especially in a reverberation environment,the separation of mixed signals is more difficult separated due to the infl...Audio signal separation is an open and challenging issue in the classical“Cocktail Party Problem”.Especially in a reverberation environment,the separation of mixed signals is more difficult separated due to the influence of reverberation and echo.To solve the problem,we propose a determined reverberant blind source separation algorithm.The main innovation of the algorithm focuses on the estimation of the mixing matrix.A new cost function is built to obtain the accurate demixing matrix,which shows the gap between the prediction and the actual data.Then,the update rule of the demixing matrix is derived using Newton gradient descent method.The identity matrix is employed as the initial demixing matrix for avoiding local optima problem.Through the real-time iterative update of the demixing matrix,frequency-domain sources are obtained.Then,time-domain sources can be obtained using an inverse short-time Fourier transform.Experi-mental results based on a series of source separation of speech and music mixing signals demonstrate that the proposed algorithm achieves better separation performance than the state-of-the-art methods.In particular,it has much better superiority in the highly reverberant environment.展开更多
High-voltage circuit breakers are the core equipment in power networks,and to a certain extent,are related to the safe and reliable operation of power systems.However,their core components are prone to mechanical faul...High-voltage circuit breakers are the core equipment in power networks,and to a certain extent,are related to the safe and reliable operation of power systems.However,their core components are prone to mechanical faults.This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring.First,a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers.Subsequently,the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals.Based on this,the recognition of the features of a single-fault voiceprint signal can be achieved.Finally,multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site.The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults,such as spring structures and loose internal components of circuit breakers.In addition,it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.展开更多
A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited...A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC (Monte Carlo Markov Chain) algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.展开更多
Blind separation of source signals usually relies either on the condition of statistically independence or involving their higher-order cumulants. The model of two channels signal separation is considered. A criterion...Blind separation of source signals usually relies either on the condition of statistically independence or involving their higher-order cumulants. The model of two channels signal separation is considered. A criterion based on correlation functions is proposed. It is proved that the signals can be separated, using only the condition of noncorrelation. An algorithm is derived, which only involves the solution to quadric nonlinear equations.展开更多
This study deals with the problem of mainlobe jamming suppression for rotated array radar.The interference becomes spatially nonstationary while the radar array rotates,which causes the mismatch between the weight and...This study deals with the problem of mainlobe jamming suppression for rotated array radar.The interference becomes spatially nonstationary while the radar array rotates,which causes the mismatch between the weight and the snapshots and thus the loss of target signal to noise ratio(SNR)of pulse compression.In this paper,we explore the spatial divergence of interference sources and consider the rotated array radar anti-mainlobe jamming problem as a generalized rotated array mixed signal(RAMS)model firstly.Then the corresponding algorithm improved blind source separation(BSS)using the frequency domain of robust principal component analysis(FDRPCA-BSS)is proposed based on the established rotating model.It can eliminate the influence of the rotating parts and address the problem of loss of SNR.Finally,the measured peakto-average power ratio(PAPR)of each separated channel is performed to identify the target echo channel among the separated channels.Simulation results show that the proposed method is practically feasible and can suppress the mainlobe jamming with lower loss of SNR.展开更多
Wavelet transform method is applied to measure time-frequency distribution characteristics of digital deformation data and noise. Based on the characteristics of primary modulus and stochastic white noise discriminati...Wavelet transform method is applied to measure time-frequency distribution characteristics of digital deformation data and noise. Based on the characteristics of primary modulus and stochastic white noise discrimination factor of wavelet decomposition, we analyze the variation rule of normal background and noise data from Shandong digital deformation observation data. The research results indicate that: a) 1/4 daily wave, semi-diurnal tide wave, daily wave and half lunar wave and so on quasi-periodic signal exist in the detail decomposing signal of wavelet when scale are equal to 2, 3 and 4; b) The amplitude of detail decomposing signal is the biggest when scale is equal to 3; c) The detail decomposing signal contains mainly noise corresponding to scale 1 and 5, respectively; d) We may trace the abnormal precursory which is related to earthquake by analyzing non-earthquake wavelet decomposing signal whose scale is specified from digital deformation observation data.展开更多
Underdetermined blind signal separation (BSS) (with fewer observed mixtures than sources) is discussed. A novel searching-and-averaging method in time domain (SAMTD) is proposed. It can solve a kind of problems ...Underdetermined blind signal separation (BSS) (with fewer observed mixtures than sources) is discussed. A novel searching-and-averaging method in time domain (SAMTD) is proposed. It can solve a kind of problems that are very hard to solve by using sparse representation in frequency domain. Bypassing the disadvantages of traditional clustering (e.g., K-means or potential-function clustering), the durative- sparsity of a speech signal in time domain is used. To recover the mixing matrix, our method deletes those samples, which are not in the same or inverse direction of the basis vectors. To recover the sources, an improved geometric approach to overcomplete ICA (Independent Component Analysis) is presented. Several speech signal experiments demonstrate the good performance of the proposed method.展开更多
Insomnia,whether situational or chronic,affects over a third of the general population in today’s society.However,given the lack of non-contact and non-inductive quantitative evaluation approaches,most insomniacs are...Insomnia,whether situational or chronic,affects over a third of the general population in today’s society.However,given the lack of non-contact and non-inductive quantitative evaluation approaches,most insomniacs are often unrecognized and untreated.Although Polysomnographic(PSG)is considered as one of the assessment methods,it is poorly tolerated and expensive.In this paper,with the recent development of Internet-of-Things devices and edge computing techniques,we propose a detrended fractal dimension(DFD)feature for the analysis of heart-rate signals,which can be easily acquired by many wearables,of good sleepers and insomniacs.This feature was derived by calculating the fractal dimension(FD)of detrended signals.For the trend component removal,we improved the null space pursuit algorithm and proposed an adaptive trend extraction algorithm.The experimental results demonstrated the efficacy of the proposed DFD index through numerical statistics and significance testing for healthy and insomnia groups,which renders it a potential biomarker for insomnia assessment and management.展开更多
Unlike the traditional independent component analysis(ICA)algorithms and some recently emerging linear ICA algorithms that search for solutions in the space of general matrices or orthogonal matrices,in this paper we ...Unlike the traditional independent component analysis(ICA)algorithms and some recently emerging linear ICA algorithms that search for solutions in the space of general matrices or orthogonal matrices,in this paper we propose two new methods which only search for solutions in the space of the matrices with unitary determinant and without whitening.The new algorithms are based on the special linear group SL(n).In order to achieve our target,we first provide a representation theory for any matrix in SL(n),which only simply uses the product of multiple exponentials of traceless matrices.Based on the matrix representation theory,two novel ICA algorithms are developed along with simple analysis on their equilibrium points.Moreover,we apply our methods to the classical problem of signal separation.The experimental results indicate that the superior convergence of our proposed algorithms,which can be expected as two viable alternatives to the ICA algorithms available in publications.展开更多
基金This research was partially supported by the National Natural Science Foundation of China under Grant 52105268Natural Science Foundation of Guangdong Province under Grant 2022A1515011409+2 种基金Key Platforms and Major Scientific Research Projects of Universities in Guangdong under Grants 2019KTSCX161 and 2019KTSCX165Key Projects of Natural Science Research Projects of Shaoguan University under Grants SZ2020KJ02 and SZ2021KJ04the Science and Technology Program of Shaoguan City of China under Grants 2019sn056,200811094530423,200811094530805,and 200811094530811.
文摘Audio signal separation is an open and challenging issue in the classical“Cocktail Party Problem”.Especially in a reverberation environment,the separation of mixed signals is more difficult separated due to the influence of reverberation and echo.To solve the problem,we propose a determined reverberant blind source separation algorithm.The main innovation of the algorithm focuses on the estimation of the mixing matrix.A new cost function is built to obtain the accurate demixing matrix,which shows the gap between the prediction and the actual data.Then,the update rule of the demixing matrix is derived using Newton gradient descent method.The identity matrix is employed as the initial demixing matrix for avoiding local optima problem.Through the real-time iterative update of the demixing matrix,frequency-domain sources are obtained.Then,time-domain sources can be obtained using an inverse short-time Fourier transform.Experi-mental results based on a series of source separation of speech and music mixing signals demonstrate that the proposed algorithm achieves better separation performance than the state-of-the-art methods.In particular,it has much better superiority in the highly reverberant environment.
基金supported by the State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks(No.SGNR0000KJJS2302137)the National Natural Science Foundation of China(Grant No.62203248)the Natural Science Foundation of Shandong Province(Grant No.ZR2020ME194).
文摘High-voltage circuit breakers are the core equipment in power networks,and to a certain extent,are related to the safe and reliable operation of power systems.However,their core components are prone to mechanical faults.This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring.First,a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers.Subsequently,the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals.Based on this,the recognition of the features of a single-fault voiceprint signal can be achieved.Finally,multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site.The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults,such as spring structures and loose internal components of circuit breakers.In addition,it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.
文摘A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC (Monte Carlo Markov Chain) algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.
文摘Blind separation of source signals usually relies either on the condition of statistically independence or involving their higher-order cumulants. The model of two channels signal separation is considered. A criterion based on correlation functions is proposed. It is proved that the signals can be separated, using only the condition of noncorrelation. An algorithm is derived, which only involves the solution to quadric nonlinear equations.
基金supported by the National Natural Science Foundation of China(62271255,61871218,61801211)the Fundamental Research Funds for the Central Universities(3082019NC2019002,NG2020001,NP2014504)+2 种基金the Open Research Fund of State Key Laboratory of Space-Ground Integrated Information Technology(2018_SGIIT_KFJJ_AI_03)the Funding of Postgraduate Research Practice&Innovation Program of Jiangsu Province(KYCX200201)the Open Research Fund of the Key Laboratory of Radar Imaging and Microwave Photonics(Nanjing University of Aeronautics and Astronautics),Ministry of E ducation(NJ20210001)。
文摘This study deals with the problem of mainlobe jamming suppression for rotated array radar.The interference becomes spatially nonstationary while the radar array rotates,which causes the mismatch between the weight and the snapshots and thus the loss of target signal to noise ratio(SNR)of pulse compression.In this paper,we explore the spatial divergence of interference sources and consider the rotated array radar anti-mainlobe jamming problem as a generalized rotated array mixed signal(RAMS)model firstly.Then the corresponding algorithm improved blind source separation(BSS)using the frequency domain of robust principal component analysis(FDRPCA-BSS)is proposed based on the established rotating model.It can eliminate the influence of the rotating parts and address the problem of loss of SNR.Finally,the measured peakto-average power ratio(PAPR)of each separated channel is performed to identify the target echo channel among the separated channels.Simulation results show that the proposed method is practically feasible and can suppress the mainlobe jamming with lower loss of SNR.
基金Natural Science Foundation of Shandong Province (Y2000E08) the bargain item of China Earthquake Administration in the year 2002.
文摘Wavelet transform method is applied to measure time-frequency distribution characteristics of digital deformation data and noise. Based on the characteristics of primary modulus and stochastic white noise discrimination factor of wavelet decomposition, we analyze the variation rule of normal background and noise data from Shandong digital deformation observation data. The research results indicate that: a) 1/4 daily wave, semi-diurnal tide wave, daily wave and half lunar wave and so on quasi-periodic signal exist in the detail decomposing signal of wavelet when scale are equal to 2, 3 and 4; b) The amplitude of detail decomposing signal is the biggest when scale is equal to 3; c) The detail decomposing signal contains mainly noise corresponding to scale 1 and 5, respectively; d) We may trace the abnormal precursory which is related to earthquake by analyzing non-earthquake wavelet decomposing signal whose scale is specified from digital deformation observation data.
基金Supported by the National Natural Science Foundation of China (Grant Nos. U0635001, 60505005 and 60674033)the Natural Science Fund of Guangdong Province (Grant Nos. 04205783 and 05006508)the Specialized Prophasic Basic Research Projects of the Ministry of Science and Technology of China (Grant No. 2005CCA04100)
文摘Underdetermined blind signal separation (BSS) (with fewer observed mixtures than sources) is discussed. A novel searching-and-averaging method in time domain (SAMTD) is proposed. It can solve a kind of problems that are very hard to solve by using sparse representation in frequency domain. Bypassing the disadvantages of traditional clustering (e.g., K-means or potential-function clustering), the durative- sparsity of a speech signal in time domain is used. To recover the mixing matrix, our method deletes those samples, which are not in the same or inverse direction of the basis vectors. To recover the sources, an improved geometric approach to overcomplete ICA (Independent Component Analysis) is presented. Several speech signal experiments demonstrate the good performance of the proposed method.
基金partly supported by the startup research funds of Nanjing University of Science and Technology。
文摘Insomnia,whether situational or chronic,affects over a third of the general population in today’s society.However,given the lack of non-contact and non-inductive quantitative evaluation approaches,most insomniacs are often unrecognized and untreated.Although Polysomnographic(PSG)is considered as one of the assessment methods,it is poorly tolerated and expensive.In this paper,with the recent development of Internet-of-Things devices and edge computing techniques,we propose a detrended fractal dimension(DFD)feature for the analysis of heart-rate signals,which can be easily acquired by many wearables,of good sleepers and insomniacs.This feature was derived by calculating the fractal dimension(FD)of detrended signals.For the trend component removal,we improved the null space pursuit algorithm and proposed an adaptive trend extraction algorithm.The experimental results demonstrated the efficacy of the proposed DFD index through numerical statistics and significance testing for healthy and insomnia groups,which renders it a potential biomarker for insomnia assessment and management.
文摘Unlike the traditional independent component analysis(ICA)algorithms and some recently emerging linear ICA algorithms that search for solutions in the space of general matrices or orthogonal matrices,in this paper we propose two new methods which only search for solutions in the space of the matrices with unitary determinant and without whitening.The new algorithms are based on the special linear group SL(n).In order to achieve our target,we first provide a representation theory for any matrix in SL(n),which only simply uses the product of multiple exponentials of traceless matrices.Based on the matrix representation theory,two novel ICA algorithms are developed along with simple analysis on their equilibrium points.Moreover,we apply our methods to the classical problem of signal separation.The experimental results indicate that the superior convergence of our proposed algorithms,which can be expected as two viable alternatives to the ICA algorithms available in publications.