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Bayesian nonstationary source separation
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DOI：10.1016/j.neucom.2007.03.012.png)
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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 vblCA-TVAR algorithm learns the temporal structure of sources and acquires cleaner source reconstruction. (c) 2007 Elsevier B.V. All rights reserved.
Keyword:
nonstationary source separation
variational Bayesian (VB) learning
time-varying autoregressive (TVAR) model
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IF:
6.5
论文数:
2.5W
被引数:
6.5W
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