Bayesian Function Learning Using MCMC Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian Unsupervised Learning for Source Separation with Mixture of Gaussians Prior
Journal of VLSI Signal Processing Systems
Inference in Hidden Markov Models (Springer Series in Statistics)
Inference in Hidden Markov Models (Springer Series in Statistics)
Temporally correlated source separation using variational Bayesian learning approach
Digital Signal Processing
MIMO-AR system identification and blind source separation for GMM-distributed sources
IEEE Transactions on Signal Processing
Bayesian separation of images modeled with MRFs using MCMC
IEEE Transactions on Image Processing
Modeling non-Gaussian time-varying vector autoregressive processes by particle filtering
Multidimensional Systems and Signal Processing
Blind MIMO-AR system identification and source separation with finite-alphabet
IEEE Transactions on Signal Processing
A bayesian approach to blind separation of mixed discrete sources by gibbs sampling
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
Blind source separation with time series variational Bayes expectation maximization algorithm
Digital Signal Processing
Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures
IEEE Transactions on Signal Processing
Blind Separation of Independent Sources Using Gaussian Mixture Model
IEEE Transactions on Signal Processing - Part II
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Bayesian blind separation of generalized hyperbolic processes in noisy and underdeterminate mixtures
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
A Bayesian Approach for Blind Separation of Sparse Sources
IEEE Transactions on Audio, Speech, and Language Processing
A Markov model for blind image separation by a mean-field EM algorithm
IEEE Transactions on Image Processing
Hidden Markov models for wavelet-based blind source separation
IEEE Transactions on Image Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
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.