Article ID Journal Published Year Pages File Type
560345 Digital Signal Processing 2007 18 Pages PDF
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.

Related Topics
Physical Sciences and Engineering Computer Science Signal Processing