Bayesian nonstationary source separation

Authors:
Qinghua Huang;Jie Yang;Yue Zhou
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, PR China;Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, PR China;Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, PR China
Venue:
Neurocomputing
Year:
2008

Citing 9
Cited 0

Blind separation of instantaneous mixture of sources via the Gaussian mutual information criterion

Signal Processing
An unsupervised ensemble learning method for nonlinear dynamic state-space models

Neural Computation
Adaptive Algorithm for Blind Separation from Noisy Time-Varying Mixtures

Neural Computation
Letters: Robust extraction of specific signals with temporal structure

Neurocomputing
Letters: Extraction of temporally correlated sources with its application to non-invasive fetal electrocardiogram extraction

Neurocomputing
Variational bayesian method for temporally correlated source separation

ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
A blind source separation technique using second-order statistics

IEEE Transactions on Signal Processing
Markovian source separation

IEEE Transactions on Signal Processing
Second-order blind separation of sources based on canonical partialinnovations

IEEE Transactions on Signal Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

A Bayesian nonstationary source separation algorithm is proposed in this paper to recover nonstationary sources from noisy mixtures. In order to exploit the temporal structure of the data, we use a time-varying autoregressive (TVAR) process to model each source signal. Then variational Bayesian (VB) learning is adopted to integrate the source model with blind source separation (BSS) in probabilistic form. Our separation algorithm makes full use of temporally correlated prior information and avoids overfitting in separation process. Experimental results demonstrate that our vbICA-TVAR algorithm learns the temporal structure of sources and acquires cleaner source reconstruction.