On-line Convolutive Blind Source Separation of Non-Stationary Signals
Journal of VLSI Signal Processing Systems
Efficient greedy learning of Gaussian mixture models
Neural Computation
Time-domain convolutive blind source separation employing selective-tap adaptive algorithms
EURASIP Journal on Audio, Speech, and Music Processing
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
Parameter estimation for autoregressive Gaussian-mixture processes: the EMAX algorithm
IEEE Transactions on Signal Processing
Blind Separation of Independent Sources Using Gaussian Mixture Model
IEEE Transactions on Signal Processing - Part II
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Performance measurement in blind audio source separation
IEEE Transactions on Audio, Speech, and Language Processing
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Blind MIMO-AR system identification and source separation with finite-alphabet
IEEE Transactions on Signal Processing
Pattern Recognition
Blind separation of non-stationary sources using continuous density hidden Markov models
Digital Signal Processing
Hi-index | 35.69 |
The problem of blind source separation (BSS) and system identification for multiple-input multiple-output (MIMO) auto-regressive (AR) mixtures is addressed in this paper. Two new time-domain algorithms for system identification and BSS are proposed based on the Gaussian mixture model (GMM) for sources distribution. Both algorithms are based on the generalized expectation-maximization (GEM) method for joint estimation of the MIMO-AR model parameters and the GMM parameters of the sources. The first algorithm is derived under the assumption of unstructured input signal statistics, while the second algorithm incorporates the prior knowledge about the structure of the input signal statistics due to the statistically independent source assumption. These methods are tested via simulations using synthetic and audio signals. The system identification performances are tested by comparison between the state transition matrix estimation using the proposed algorithms and the well-known multidimensional Yule-Walker solution followed by an instantaneous BSS method. The results show that the proposed algorithms outperform the Yule-Walker based approach. The BSS performances were compared to other convolutive BSS methods. The results show that the proposed algorithms achieve higher signal-to-interference ratio (SIR) compared to the other tested methods.