A new perceptual convolutive blind source separation algorithm for speech separation

Blind Signal Separation II. Linear, Convolutive Combinations: II. Linear, Convolutive Combinations

We present an efficient algorithm for the blind signal separation (BSS) problem with convolutive signal mixtures, as it usually appears in acoustics, e.g., in the cocktail party problem. Since acoustical signals are typically nonstationary and nonwhite, we make use of these two statistical properties in the formulation of the blind cost function. In order to achieve true signal separation, the algorithm aims at finding a single polynomial matrix, the convolutive separation matrix that jointly diagonalizes a set of measured spatiotemporal correlation matrices. Minimizing the cost function turns out to be mathematically equivalent to a convolutive joint approximate diagonalization problem (CJAD). In order to increase the initial convergence rate, an Armijo line search is incorporated into the update. The final algorithm operates primarily in the frequency domain (fast convolution techniques) even though the cost function and gradient are formulated in the time domain. This approach reduces the computational complexity and avoids the so-called permutation problem.

The aim of this paper is to study blind separation of speech signals based on feedforward neural network with sine activation function.The natural gradient is a major contribution to blind source separation (BSS) because its performance is independent of the mixing matrix.Computer simulations are provided by speech signals as sources (ten speakers) and in the instantaneous mixing case show that the algorithm with sine activation function converges faster and recovers independent sources better than other activation functions.

The compound noises in circuit deteriorate the output signals. This paper proposes a mutual information based separation (MISEP) method of the blind signal separation (BSS) problem for the circuits with the instant mixing model. Both the linear and the nonlinear circuits are taken as the circuits under experiment, and both the signals and the noises are regarded as the inputs of the circuits. Experimental results verify the effectiveness of the proposed method, because the different inputs of the circuits are identified by the time domain waveforms and the power spectrum characteristics.

We consider the problem of blind adaptive signal separation with an antenna array, based on the constant modulus (CM) criterion. An approximation to the CM cost function is proposed, which allows the use of the recursive least squares (RLS) optimization technique. A novel RLS constant modulus algorithm (RLS-CMA) is derived, where the modulus power of the array output can take on arbitrary positive real values (i.e., fractional values allowed). Simulations are performed to compare the performance of the proposed RLS-CMA to other well-known algorithms for blind adaptive beamforming. Results indicate that the RLS-CMA has a significantly faster convergence rate and better tracking ability.

Support Vector Machines (SVM) and Independent Component Analysis (ICA) are two powerful and relatively recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. ICA is a feature extraction technique which can be considered a generalization of Principal Component Analysis (PCA). ICA has been mainly used on the problem of blind signal separation. In this paper we combine these two techniques for the face recognition problem. Experiments were made on two different face databases, achieving very high recognition rates. As the results using the combination PCA/SVM were not very far from those obtained with ICA/SVM, our experiments suggest that SVMs are relatively insensitive to the representation space. Thus as the training time for ICA is much larger than that of PCA, this result indicates that the best practical combination is PCA with SVM.KeywordsSupport Vector MachineFace RecognitionFace ImageIndependent Component AnalysisFace DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

To solve the problem of single channel blind signal separation with unknown components number, a method based on Bayesian-MCMC is presented. First, single channel mixed signal, whose components have same general parameters, is modeled to change the problem into joint estimation of signal parameters and components number. Then, Bayesian theorem is applied in the joint estimation. Lastly, Bayesian computation of the estimation is accomplished with reversible jump MCMC. Simulation results indicate that the algorithm is practical and effective.

The blind separation of speech signals in reverberant environments is a well-known problem for which many algorithms have been developed. In this paper, we propose a novel initialization procedure for those ICA algorithms that work in the time-frequency domain and use the prewhitening of the observations. In comparison with classical initializations, this method allows to reduce drastically the number of permutations. The effectiveness of the proposed technique in realistic scenarios is illustrated by means of simulations.

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

The role of the intelligent optimization algorithm in the blind separation algorithm is to make the independence decision function enter the global optimal position, but the performance of many optimization algorithms depends on the choice of control parameters. In this paper, we use the simplex neighborhood convex set approximation idea, combined with the full random search and multi-role state of the search operator, we use a single-parameter surface-simplex swarm evolution (SSSE), which has a single parameter and pure randomness. The characteristics of the search can overcome the shortcomings of the algorithm’s dependence on the control parameters and easily fall into the local optimum, and improve the application effectiveness of the blind separation algorithm. In the experiment, the fourth-order cumulant (kurtness) is used as the criterion function in ICA, and the improved blind separation algorithm is applied to the blind separation of speech and background music signals. Experimental results show that the improved algorithm effectively separates speech components from the background. The music component and the algorithm have better performance in terms of convergence speed, algorithm stability, and separation effect.

The problem of blind separation of signals in a post-nonlinear mixture is addressed in this paper. A post-nonlinear mixture is formed by a component-wise nonlinear distortion of a linear mixture. Hence, a nonlinear adjusting part, placed in front of the linear separation structure, is needed to compensate for the distortion in separating such signals. The learning rules for the post-nonlinear separation structure are derived by a maximum likelihood approach. An algorithm for the blind separation of post-nonlinearly mixed suband super-Gaussian signals is proposed, based on some previous works. Multilayer perceptrons are used in this algorithm to model the nonlinear part of the separation structure. The algorithm switches between sub- and super-Gaussian probability models during learning according to a stability condition, and it operates in a block-adaptive manner. The effectiveness of the algorithm is verified by experiments on artificial and natural signals.

Upgrading Sparse NMF algorithm for blind source separation through Adaptive Parameterized Hybrid Kernel based approach

In order to overcome the problem of low accuracy and poor stability caused by the one-sided information blind separation analysis of the current communication channel interference suppression research methods, this paper proposes the optical communication network channel interference suppression method based on blind signal separation algorithm. The receiver unit, field programmable gate array (FPGA) unit, memory chip group and other modules are used to construct the channel signal acquisition system, and the collected signals are preprocessed by centralisation and whitening. Based on the signal preprocessing results, the blind separation of instantaneous linear mixed signals, blind separation of convolutional mixed signals and blind separation of sparse sources were comprehensively analysed to obtain the source signals and realise channel interference suppression of optical communication network. Experimental results show that this method has high interference precision, strong stability and good application prospect.

In this paper, the blind signal separation problem of complex baseband signal is addressed. A widely linear complex autoregressive process of order one is employed to represent the temporal structure of complex sources. We formulate a new contrast function by a convex combination of generalized autocorrelations and the statistics of the innovation. And the proposed contrast function is optimized by gradient method. Simulation results show that the proposed algorithm is better than the comparison algorithm in convergence speed and convergence accuracy.

Under-determined blind separation becomes poorer with increasing number of tags, to the point where it cannot separate source tag signals, reducing overall system performance. This paper proposes a parallelizable identification anti-collision algorithm based on non-negative matrix factorization and adaptive ID sequence grouping of binary tree slots. The number of tags in each group can be controlled within the optimum range by selecting a reasonable number to retained source signal separation in the RFID system, which will greatly improve system performance. With the Matlab software numerical calculation and simulation, the results show that tag identification rate improves from 152.8% to 359.2% compared with the blind separation and dynamic bit-slot group algorithm using the same multi-antenna technology for 4–16 antennas, while increasing tag identification speed from 60% to 78.4%. Thus, the proposed algorithm provides high efficiency and low cost and will have very good application for fast identification of large numbers of tags.