Blind Separation Algorithm Research Articles

An efficient wavelet-based adaptive filtering algorithm for automatic blind speech enhancement

In this paper, we address the problem of speech enhancement by adaptive filtering algorithms. A particular attention has been paid to the backward blind source separation (BBSS) algorithm and its use in crosstalk resistant speech enhancement applications. In this paper, we propose to implement the BBSS algorithm in the wavelet-domain. The proposed backward wavelet BBSS (WBBSS) algorithm is then used in speech enhancement application when important crosstalk interferences are presents. The new WBBSS algorithm shows better performances in terms of convergence speed and steady state in comparison with the classical BBSS one. The performances properties of the proposed algorithm are evaluated in term of segmental SNR (SegSNR), segmental mean square error (SegMSE), and cepstral distance (CD) criteria. The obtained results have confirmed the best performance of the proposed WBBSS algorithm in a lot of situations when blind noisy observations are available.

Blind separation of asymmetric signals based on Synchrosqueezing Wavelet Transform

A blind separation algorithm based on synchronous squeezing wavelet transform is proposed to solve the blind separation problem of single channel asymmetric signals in satellite communications. First, the algorithm is used to synchronize the strong signal. Then, the signal time-frequency curve is extracted by synchronous extrusion wavelet transform. Finally, the weak signal interference is filtered out from the mixed signal except the noise and the main frequency of the strong signal. Therefore, the ber of the strong signal demodulation is reduced. The algorithm has the characteristics of blind separation of single channel asymmetric signals without prior information sequence sent by cooperative communicators. The simulation results show that the performance of strong signal demodulation error is better than that of mixed signal direct hard decision.

Baseband Communication Signal Blind Separation Algorithm Based on Complex Nonparametric Probability Density Estimation

In this paper, a blind separation algorithm based on nonparametric probability density estimation (NPDE) for baseband communication signals is proposed. The NPDE method can precisely estimate the probability density of signals, and its good data-driven property guarantees the adaptability of the proposed algorithm. The NPDE method is extended to complex field to fit the complex characteristics of baseband communication signals. The complex update procedure of demixing matrix corresponding to complex channel matrix and signals is also derived in this paper. The experimental results indicate that the proposed algorithm not only can effectively separate the mixture of baseband communication signals, which are modulated with the same or different modulation schemes, but also has better convergence properties and greater signal interference ratio gains than the contrast algorithms. Moreover, even if the local carrier of receiver is unsynchronized to the carrier of received signals due to carrier offsets, the proposed algorithm can also achieve a valid separation.

Noisy blind source separation based on CEEMD and Savitzky-Golay filter

The standard independent component analysis (ICA) algorithm is difficult to extract signals in noise condition, a blind separation algorithm based on denoising pretreatment was proposed. Mixed signals firstly were decomposed into several stationary intrinsic mode components (IMF) using complementary ensemble empirical mode decomposition (CEEMD), and high frequency IMF components were filtered with Savitzky-Golay filtering, then using the whole components reconstructed the mixed signals, finally applying the fast independent component analysis(FastICA) to separate the reconstructed signals. Simulation results showed that the proposed method improved the effect of blind signal separation under low signal-to-noise ratio.

A local entropy-based algorithm for information content extraction from time–frequency distributions of noisy signals

In this paper, an automatic adaptive method for identification and separation of the useful information content, from the background noise of time–frequency distributions (TFD) of multicomponent nonstationary signals, is presented. The method is based on an initial segmentation of the TFD information content by the K-means clustering algorithm, that partitions the initial data set in order to obtain K classes containing elements with similar amplitudes. It is shown that the local Rényi entropy (LRE) can accurately distinguish classes containing noise from classes with the useful information content, as a consequence of their basic structural differences in the time–frequency plane. Simulations are run to compare the performance of the proposed adaptive algorithm for blind separation of useful information from background noise (i.e. blind amplitude threshold) and non-adaptive (hard) amplitude TFD threshold procedures. Simulation results indicate that the proposed method performs better or closely to the best of five blindly chosen hard thresholds. The limitation of efficient hard-thresholding is the need of previous knowledge of the signal's structure and SNR or visual evaluation.

Algorithm for blind separation of PCMA based on CHASE decoding

Algorithm for blind separation of PCMA based on CHASE decoding

Source separation employing beamforming and SRP-PHAT localization in three-speaker room environments

This paper presents a new blind speech separation algorithm using beamforming technique that is capable of extracting each individual speech signal from a mixture of three speech sources in a room. The speech separation algorithm utilizes the steered response power phase transform for obtaining a localization estimate for each individual speech source in the frequency domain. Based on those estimates each desired speech signal is extracted from the speech mixture using an optimal beamforming technique. To solve the permutation problem, a permutation alignment algorithm based on the mutual output correlation is employed to group the output signals into the correct sources from each frequency bin. Evaluations using real speech recordings in a room environment show that the proposed blind speech separation algorithm offers high interference suppression level whilst maintaining low distortion level for each desired signal.

Electroencephalogram-Artifact Extraction Enhancement Based on Artificial Intelligence Technique

Blind source separation (BSS) is an important technique used to recover isolated independent sources signals from mixtures. This paper proposes two blind artificial intelligent separation algorithms based on hybridization between artificial intelligent techniques with classical blind source separation algorithms to enhance the separation process. Speedy genetic algorithm SGA directly guesses the optimal coefficients of the separating matrix based on candidate initial from classical BSS algorithms also the separation criteria based on minimization of mutual information between the separating independent components. The proposed algorithms are tested by real Electroencephalogram (EEG) data, the experimental results indicate that the algorithms can quickly and effectively get optimum solution to linear blind source separation compared to classical BSS techniques, the proposed works are described by high accuracy and robustness.

Fixed-point ICA algorithm for blind separation of complex mixtures containing both circular and noncircular sources

Fixed-point algorithms are widely used for independent component analysis (ICA) owing to its good convergence. However, most existing complex fixed-point ICA algorithms are limited to the case of circular sources and result in phase ambiguity, that restrict the practical applications of ICA. To solve these problems, this paper proposes a two-stage fixed-point ICA (TS-FPICA) algorithm which considers complex signal model. In this algorithm, the complex signal model is converted into a new real signal model by utilizing the circular coefficients contained in the pseudo-covariance matrix. The algorithm is thus valid to noncircular sources. Moreover, the ICA problem under the new model is formulated as a constrained optimization problem, and the real fixed-point iteration is employed to solve it. In this way, the phase ambiguity resulted by the complex ICA is avoided. The computational complexity and convergence property of TS-FPICA are both analyzed theoretically. Simulation results show that the proposed algorithm has better separation performance and without phase ambiguity in separated signals compared with other algorithms. TS-FPICA convergences nearly fast as the other fixed-point algorithms, but far faster than the joint diagonalization method, e.g. joint approximate diagonalization of eigenmatrices (JADE).

Blind separation of multicomponent seismic wavefield using SVD of reduced dimension spectral matrix

This paper presents a blind separation algorithm based on singular value decomposition (SVD) of reduced dimension spectral matrix. Furthermore, a mathematical matrix model of the multicomponent seismic wavefield is developed as a framework for implementing the proposed algorithm. The proposed blind separation algorithm organizes the frequency transformed multicomponent seismic wavefield into one long data vector. The blind separation of the desired seismic wavefield is accomplished by projecting the long data vector onto the eigenvectors of the dimensionally reduced spectral matrix according to the energy of the eigenvalues. The proposed algorithm is tested on both synthetic and real multicomponent seismic wavefields. Results show outstanding performance compared to the MC-WBSMF algorithm. Therefore, the computational complexity is reduced by a factor greater than 14,400 and there is an improvement in accuracy of 17.5%.

Glowworm Swarm Optimization and Its Application to Blind Signal Separation

Traditional optimization algorithms for blind signal separation (BSS) are mainly based on the gradient, which requires the objective function to be continuous and differentiable, so the applications of these algorithms are very limited. Moreover, these algorithms have problems with the convergence speed and accuracy. To overcome these drawbacks, this paper presents a modified glowworm swarm optimization (MGSO) algorithm based on a novel step adjustment rule and then applies MGSO to BSS. Taking kurtosis of the mixed signals as the objective function of BSS, MGSO-BSS succeeds in separating the mixed signals in Matlab environment. The simulation results prove that MGSO is more effective in capturing the global optimum of the objective function of the BSS algorithm and has faster convergence speed and higher accuracy, compared with particle swarm optimization (PSO) and GSO.

基于去噪降维和蝙蝠优化的高光谱图像盲解混算法

基于去噪降维和蝙蝠优化的高光谱图像盲解混算法

Blind Separation of Permuted Alias Image Based on K-SVD Dictionary Learning

In this work, a new blind separation algorithm for permuted alias image based on dictionary learning is proposed according to a type of permuted alias image with noise diversity. Sparse representation of permuted alias image is obtained by dictionary learning method, since it has high adaptability and its representation result has higher sparsity degrees than that of parameter dictionary. An optimal permuted alias image is achieved by conducting sparse representation with K-SVD dictionary learning algorithm restrained with nonzero element number. The size and the location of permuting region is found by detecting the subtraction image, which is defined as the difference between the reconstructed permuted alias image and the original permuted alias image. The permuting region is optimized by implementing image morphological operation and is separated from the permuted alias image by the threshold. Experimental results show that the permuting sub-images can be efficiently separated from the permuted alias image, which is not affected by the size, location, number of permuting sub-images and noise level of the permuting sub-images.

A Fast Algorithm for Blind Separation of Complex Valued Signals with Nonlinear Autocorrelation

A Fast Algorithm for Blind Separation of Complex Valued Signals with Nonlinear Autocorrelation

Improved Natural Gradient Algorithms for Multi-Channel Signal Separation

In this paper, an Multi-channel blind separation of Direct Sequence Code Division Multiple Access (DS-CDMA) has been introduced. Most of which we assume statistically stationary sources as well as instantaneous mixtures of signals using blind source separation (BSS) algorithms. In practicality, the CDMA sources received are convolute mixing. A more complex blind separation algorithm is required to achieve better source separation. Based on the minimizing the average squared cross-output-channel-correlation, the proposed scheme obtains the better source separation. Simulation results show that the proposed optimal scheme not only achieves superior bit error rate (BER) performance to those of the existing ones, but also provides a guaranteed-convergent solution under different channel conditions.

New automatic forward and backward blind sources separation algorithms for noise reduction and speech enhancement

In this paper, we consider the speech enhancement problem in a moving car through a blind source separation (BSS) scheme involving two spaced microphones. The forward and backward blind source separation structures often use manual voice activity detector (MVAD) systems to control the adaptation of the separating adaptive filters. In this paper, we propose two new automatic voice activity detector (AVAD) systems that allow adapting the original forward and backward BSS structures automatically. The proposed AVAD systems are based on the use of the forward BSS structure to estimate the optimal values of the separating adaptive filters step-sizes. Moreover, the new proposed algorithms are stable and could be used even in very noisy conditions. Intensive experiments are carried out with these two new proposed algorithms to validate their good performances in speech enhancement and noise reduction applications. The presented experiments are based on the system mismatch, the cepstral distance and the output signal-to-noise ratio criteria evaluations. The obtained results show the good performances of the proposed algorithms in comparison with their original versions, where manual VAD systems are used.

The Performance Evaluation of Classic ICA Algorithms for Blind Separation of Fabric Defects

The Performance Evaluation of Classic ICA Algorithms for Blind Separation of Fabric Defects

A Neural Learning Algorithm of Blind Separation of Noisy Mixed Images Based on Independent Component Analysis

Blind source separation problem has recently received a great deal of attention in signal processing and unsupervised neural learning. In the current approaches, the additive noise is negligible so that it can be omitted from the consideration. To be applicable in realistic scenarios, blind source separation approaches should deal evenly with the presence of noise. In this contribution, we proposed approaches to independent component analysis when the measured signals are contaminated by additive noise. A noisy multiple channels neural learning algorithm of blind separation is proposed based on independent component analysis. The data have no noise are used to whiten the noisy data, and the windage wipe off technique is used to correct the infection of noise, a neural network model having denoise capability is adopted to recover some original signals from their noisy mixtures observed by the same number of sensors. And a relaxation factor is introduced into the iteration algorithm, thus the new algorithm can implement convergence. Computer simulations and experiment results prove the feasibility and validity of the neural network modeling and control method based on independent component analysis, which can renew the original images effectively.

Blind Separation of Synchronous-networking Frequency Hopping Signals based on Time-frequency Analysis

A novel method on smoothed-pseudo Wigner-Ville Distribution (SPWVD) to extract single-source zone of the synchronous-networking frequency hopping (FH) signals is proposed in this paper. Since the hop rate and hop period of synchronous-networking FH signals are the same, the method is applied combining with these characteristics. Compare with other methods such as independent component analysis (ICA), the algorithm takes the advantage of the sparse distribution of FH signal in frequency. Then the blind separation algorithm of FH signals with time-frequency ration of matrix (TIFROM) based on short time Fourier transform (STFT) is improved. Simulation results show that the proposed algorithm is effective in the separation of synchronous-networking FH signals, even though the interval between FH signals is relatively small.

Efficient Blind Spectral Unmixing of Fluorescently Labeled Samples Using Multi-Layer Non-Negative Matrix Factorization

The ample variety of labeling dyes and staining methods available in fluorescence microscopy has enabled biologists to advance in the understanding of living organisms at cellular and molecular level. When two or more fluorescent dyes are used in the same preparation, or one dye is used in the presence of autofluorescence, the separation of the fluorescent emissions can become problematic. Various approaches have been recently proposed to solve this problem. Among them, blind non-negative matrix factorization is gaining interest since it requires little assumptions about the spectra and concentration of the fluorochromes. In this paper, we propose a novel algorithm for blind spectral separation that addresses some of the shortcomings of existing solutions: namely, their dependency on the initialization and their slow convergence. We apply this new algorithm to two relevant problems in fluorescence microscopy: autofluorescence elimination and spectral unmixing of multi-labeled samples. Our results show that our new algorithm performs well when compared with the state-of-the-art approaches for a much faster implementation.