Classical Blind Source Separation Algorithms Research Articles

Blind image separation based on attentional generative adversarial network

Mixing signal separation is an important field of image processing. However, traditional blind source separation (BSS) algorithms were proposed to solve this task utilizing multiple signal constraints such as independent, non-Gaussian, low rank, sparsity, temporal continuity etc. What’s more, as a case of ill-conditioned signal mixing, the single-channel blind source separation (SCBSS) is more difficult. Because neural networks have strong adaptability and self-organization capability, neural network methods based on training and learning ideas are favored by researchers. However, most BSS methods based on neural network are limited by small sample sizes. Among various neural network, generative adversarial network (GAN) has emerged as an interesting candidate because it is free from statistical constraints and samples. Therefore, we present a single-channel blind image separation algorithm based on attention mechanism GAN, coined AGAN, which uses an end-to-end manner, and it will have more hopeful prospects in the blind image separation task. The network with feature extraction, as well as edge guidance to data creates a new way to iteratively separate mixing images. The experimental results show that AGAN can effectively separate the source signal in the mixing images compared with the neural egg separation (NES) algorithm, which is a neural network separation algorithm. Compared with the classical blind source separation algorithms, this method has better separation performance.

A Low-Complexity PARAFAC Decomposition for Underdetermined Blind System Identification with Complex Mixtures

A new method to effectively reduce the complexity for underdetermined blind system identification with complex mixtures is proposed in this paper. Generally speaking, the identifiability of a MIMO system can be guaranteed by decomposing a tensor constructed by the cumulant of the observations in an appropriate order, but higher order statistics will bring heavier computation load as well as more estimation errors. In the proposed method, by stacking two order-K tensors with $$K-1$$ identical factor matrices, a new tensor can be constructed followed by a PARAFAC decomposition, in which way the required minimum order of statistics can be reduced for a given underdetermined system. Experiments conducted on both the order-3 and order-4 tensors-stacking demonstrate the merits of the proposed algorithm in reducing the computational complexity while not degrading the identification performance in comparison with the standard alternating least squares algorithm. Specifically, in the $$4 \times 3$$ and $$6 \times 3$$ underdetermined systems, the numerical complexity can be reduced by more than $$50\%$$ and $$90\% $$ , respectively. Furthermore, the proposed algorithm outperforms the classical blind source separation algorithms in the determined and overdetermined cases especially in low SNRs.

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.

Supervised and unsupervised separation of convolutive speech mixtures using f 0 and formant frequencies

In this paper we discuss the role of fundamental frequency f0 and formants F1, F2 and F3 of the speech signal in supervised and unsupervised source separation of real recorded convolutive speech mixtures. Initially supervised source separation is discussed where it is assumed that sources are known a priori. The supervised source separation is discussed by considering (1) only fundamental frequency f0, (2) only formants F1, F2 and F3, (3) both f0 and formants F1, F2 and F3. It is observed that last case which involves both f0 and formants gives most accurate separation results and is used as ideal case or reference to compare the separation results obtained for unsupervised source separation. The unsupervised source separation is discussed, where there is no knowledge about the sources a priori. The unsupervised source separation is discussed using (1) cross correlation of formants of different frames along with f0 and (2) standard deviation of magnitude of frequency components in F1, F2 and F3 regions of the spectrogram. It is observed that separation results obtained using both unsupervised methods are very close to the ideal case in supervised source separation. The results show that this method works better than some of the classical blind source separation algorithms like independent component analysis and non negative matrix factorization which works well only for the case of instantaneous mixtures where delay is neglected.

Blind separation of non-stationary sources using continuous density hidden Markov models

Blind source separation (BSS) has attained much attention in signal processing society due to its 'blind' property and wide applications. However, there are still some open problems, such as underdetermined BSS, noise BSS. In this paper, we propose a Bayesian approach to improve the separation performance of instantaneous mixtures with non-stationary sources by taking into account the internal organization of the non-stationary sources. Gaussian mixture model (GMM) is used to model the distribution of source signals and the continuous density hidden Markov model (CDHMM) is derived to track the non-stationarity inside the source signals. Source signals can switch between several states such that the separation performance can be significantly improved. An expectation-maximization (EM) algorithm is derived to estimate the mixing coefficients, the CDHMM parameters and the noise covariance. The source signals are recovered via maximum a posteriori (MAP) approach. To ensure the convergence of the proposed algorithm, the proper prior densities, conjugate prior densities, are assigned to estimation coefficients for incorporating the prior information. The initialization scheme for the estimates is also discussed. Systematic simulations are used to illustrate the performance of the proposed algorithm. Simulation results show that the proposed algorithm has more robust separation performance in terms of similarity score in noise environments in comparison with the classical BSS algorithms in determined mixture case. Additionally, since the mixing matrix and the sources are estimated jointly, the proposed EM algorithm also works well in underdetermined case. Furthermore, the proposed algorithm converges quickly with proper initialization.

Developing an audio-visual speech source separation algorithm

Looking at the speaker’s face is useful to hear better a speech signal and extract it from competing sources before identification. This might result in elaborating new speech enhancement or extraction techniques exploiting the audio-visual coherence of speech stimuli. In this paper, a novel algorithm plugging audio-visual coherence estimated by statistical tools on classical blind source separation algorithms is presented, and its assessment is described. We show, in the case of additive mixtures, that this algorithm performs better than classical blind tools both when there are as many sensors as sources, and when there are less sensors than sources. Audio-visual coherence enables a focus on the speech source to extract. It may also be used at the output of a classical source separation algorithm, to select the “best” sensor with reference to a target source.

A maximum a posteriori estimate for the source separation problem with statistical knowledge about the mixing matrix

In the blind source separation (BSS) problem, nothing is supposed about the mixing matrix entries. When a prior knowledge about their values is available, the BSS algorithms can be optimized considering this information. We obtain the maximum a posteriori estimate of the source separation problem for prewhitened observed signals and prior statistical knowledge about the mixing matrix for the real, linear, instantaneous case. As new information is included in the formulation of the problem, the variance of classical BSS algorithms can be reduced.