Abstract
Blind source separation (BSS) has attracted dramatic research interests in the past decade due to its potential applications in signal processing, telecommunications, and medical imaging. Among the open issues in BSS is how to recover the source signals from the linear convolutive mixtures which are observed by an array of sensors, and this remains a challenging problem. An effective solution is to transform the convolutive model into the frequency domain so that a series of complex-valued instantaneous BSS can be applied independently to each frequency bin. This has simplified the separation problem with a better convergence performance. However, a crucial problem, called the permutation problem, should be solved before gaining a good separation performance. This talk gives an outline of our approach to the frequency domain BSS with emphasis on the solutions to the permutation problem. Some recent results, together with a comparative discussion of the state-of-the-art approaches will be presented.