Temporally correlated source separation using variational Bayesian learning approach

Authors:
Qinghua Huang;Jie Yang;Shoushui Wei
Affiliations:
Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, People's Republic of China;Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, People's Republic of China;School of Control Science and Engineering, Shandong University, Shandong 250061, People's Republic of China
Venue:
Digital Signal Processing
Year:
2007

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Abstract

Basic blind source separation (BSS) algorithms did not adopt time information of signals. They assumed that each source was independent and identically distributed (i.i.d.). In the paper, we propose to use time structure and prior information of sources in order to improve separation. Modeling source by generalized autoregressive (GAR) process, we can tackle the problem of temporally correlated source separation using variational Bayesian (VB) learning approach. The advantages of our proposed algorithm are that (i) it makes full use of time structure of sources; (ii) it can separate different type of sources in noisy environment; (iii) it can avoid overfitting in separation. Experimental results demonstrate that our algorithm outperforms VB separation algorithm based on i.i.d. source model and second-order statistical decorrelation algorithm.