Determined Reverberant Blind Source Separation of Audio Mixing Signals

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.

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

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.

Single channel signal component separation using Bayesian estimation

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.

Criterion for Blind Signals Separation Based on Correlation Function

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.

APPLICATION OF FM^mlet TRANSFORM TO SIGNAL SEPARATION

Since the multicomponent signal with nonlinear time-frequency structures occupies a wide frequency band, and the spectral contents may alias, it is therefore difficult to separate the signal components and to separate the signal from Ijackground noise. In this paper, a new signal separation method using FMmlet transform is proposed by taking the advantage that the atoms of FMm let transform can match both the linear and nonlinear time-varying structures. Theoretical predictions and numerical experiments show the feasibility of the methodology advocated.

BLIND SIGNAL SEPARATION OF LINEAR MIXTURE USING TRILINEAR DECOMPOSITION

This paper introduces a new source separation technique exploiting the time coherence of the source signals. The proposed approach relies only on stationary second order statistics. Blind Signal Separation (BSS) method using trilinear decomposition is proposed in this paper. Simulation results reveal that our proposed algorithm has the better blind signal separation performance than joint diagonalization method. Our proposed algorithm does not require whitening processing. Moreover, our proposed algorithm works well in the underdetermined condition, where the number of sources exceeds than the number of sensors.

Fractal Uniform Circular Arrays Based Multi-Orbital-Angular-Momentum-Mode Multiplexing Vortex Radio MIMO

A radio wave driven by Orbital angular momentum(OAM) is called a vortex radio and has a helical wavefront. The differential helical wavefronts of several vortex radios are closely related to their topological charges or mode numbers. In physics, two or more radio waves with different mode numbers are orthogonal to their azimuth angles. With the development of radio communication technologies, some researchers have been exploring the OAM-based multi-mode multiplexing(multi-OAM-mode multiplexing) technologies in order to enhance the channel spectrum efficiency(SE) of a radio communication system by using the orthogonal properties of vortex radios. After reviewing the reported researches of OAM-based radio communication, we find that some breakthroughs have been made in the combination of OAM and traditional Multi-Input-Multi-Output(MIMO). However, the existing technology is not sufficient to support OAM-based MIMO system to achieve maximum the channel SE. To maximize the spectrum efficiency of OAM-based MIMO system, we present a reused multi-OAM-mode multiplexing vortex radio(RMMVR) MIMO system, which is based on fractal uniform cir-cular arrays(UCAs). The scheme described in this study can effectively combine multiOAM-mode multiplexing with MIMO spatial multiplexing. First, we present the generation of RMMVR MIMO signals. Second, under line-of-sight(LOS) propagation conditions, we derive the channels of the RMMVR MIMO system. Third, we separate the RMMVR MIMO signals using an orthogonal separation method based on full azimuth sampling. Finally, we introduce the method for calculating the channel capacity of the RMMVR MIMO system. Theoretical analysis shows that the scheme proposed in this study is feasible. Moreover, the simulation results show that spatial and mode diversity are obtained by exploiting fractal UCAs. However, to enhance the channel SE of RMMVR MIMO system, an interference cancellation method needs to be introduced for zero-mode vortex radios, and some methods of multi-OAM-mode beams convergence and mode power optimization strategy should be introduced in the future.

Mainlobe jamming suppression via improved BSS method for rotated array radar

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.

Research on black-and-white image processing method of smart car camera

In view of the images collection and processing problems of the smart car camera, the paper introduces a method which deals with field and line synchronization signal separation and binarization processing of the video signal collected from track fields, and which is capable to extract and position black border trajectory images effectively. According to the experiment results of the method, the camera images can be collected and processed effectively, and the accurate image information can be provided for the smart cars to travel along the track. The method has the advantages of being easy to use, strong adaptability, ideal performance and high practical value. On the basis of advantages the method is of high practical value in smart car races.

Analyses on normal background characteristics about deformation observation data on the basis of wavelet transform method

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.

Searching-and-averaging method of underdetermined blind speech signal separation in time domain

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.

Heart-Rate Analysis of Healthy and Insomnia Groups with Detrended Fractal Dimension Feature in Edge

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.

NEW ICA ALGORITHMS BASED ON SPECIAL LINEAR GROUP

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.