Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
The Variational Bayes Method in Signal Processing (Signals and Communication Technology)
The Variational Bayes Method in Signal Processing (Signals and Communication Technology)
Variational Bayes for generalized autoregressive models
IEEE Transactions on Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Second-order blind separation of sources based on canonical partialinnovations
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
An alternative FIR filter for state estimation in discrete-time systems
Digital Signal Processing
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In the paper, to exploit the temporal information of signal, an autoregressive (AR) process is adopted to model the time structure of each source signal. Then variational Bayesian (VB) approach is used to separate noisy mixtures of temporally correlated sources. We express noisy mixing model and AR source model in a state space form and employ variational Kalman smoother to estimate source. The advantage of our algorithm is that it exploits the temporally correlated nature of source signal. Experiments on artifact and real-world speech signals are used to verify our proposed algorithm. As a result, AR source model improves the separation. The performance of our algorithm is compared with that of VB separation algorithm based on independent and identically distributed (i.i.d.) assumption which each source satisfies and the second-order blind identification (SOBI) algorithm.